Exploring a systemic functional semiotics approach to understanding emotional expression in singing performance: Implications for music education

Abstract

Engagement with songs through performance and analysis is a key component of music curricula worldwide. Music learning has a significant impact on a number of student competencies, including enhancing students’ communicative abilities as they learn to manipulate, express, and share sound in both voice qualities and lyrics. However, common analyses of singing performance rarely focus exclusively on voice quality, and there is no systematic framework which considers how emotional meaning in lyrics interacts with emotional meaning in voice quality. Drawing on systemic functional semiotics, this article proposes a unified theoretical framework for examining how emotional meaning is co-constructed in the voice and lyrics in singing performance. This framework provides a novel approach for discussing and teaching song analysis and performance. The framework will be illustrated through the analysis of the interaction between voice quality and lyrics in the song “Someone Like You” performed by Adele.

Keywords

emotion expressivity lyrics music literacy education popular music sung voice systemic functional semiotics

Context of the study

Music is a moral law. It gives soul to the universe, wings to the mind, and life to everything. Without music, life would be an error.

The above quote opens “England’s National Plan for Music Education,” establishing the importance of, and rationale for, participation in classroom-based music (English Department for Education, 2011). Among its many developmental benefits, music education enriches students’ lives, empowers them to “reach their creative and expressive potential,” and enhances their “language and literacy competence” (Phillips & Doneski, 2015, p. 195; Smith, 2006, p. 28). In the educational context, singing is a very accessible (Davis, 2017, p. 445; Nichols, 2017) and widely used (Ashley, 2015; Bonshor, 2017; Dalladay, 2017; Wicks, 2015) classroom-based music activity. Singing is of low cost to schools and students can often learn songs quickly (Hughes, 2007, p. 41; Pihkanen, 2011, p. 42). Of all genres, Contemporary Commercial Music (CCM), which includes popular songs and ballads, is the most ubiquitous in many students’ everyday lives. Due to the widespread engagement with this kind of music among students, particularly in the upper years of school, these songs are perhaps most likely to resonate with most students’ musical taste. Using CCM songs in music education, therefore, can create a highly engaging learning context. Furthermore, as CCM song lyrics are more akin to the everyday language of young adults, they also create valuable opportunities to enhance students’ language and literacy skills simultaneously with music learning. This potential for CCM to be used to deliver comprehensive education within the school system has resulted in it growing in popularity internationally (see, for example, Väkevä, 2006).

Music curricula around the world share the same requirements for understanding, performing, and creating music, especially in relation to songs (e.g., Australian Curriculum, n.d.; Canadian Department of Education, n.d.; English Department for Education, 2011; New York State School Music Association, 2012; Singaporean Ministry of Education, 2016). To meet such requirements, especially in the upper years, the accurate performance of individual musical elements alone is insufficient (see Table 1). Rather, optimal emotional expression is predicated on combining elements in unique and additive ways (Juslin & Laukka, 2003, p. 801). In CCM, this regularly results in “idiosyncratic and non-standardised” forms of musical expression (Green, 2008, p. 8), which are often central to a performer’s musical identity (Keskinen, 2013, p. 38).

Table 1.

General Requirements of Music Curricula Around the World in Relation to Song.

Lower years of schooling	Centered on mastery of the musical elements, which typically refers to pitch, tempo, timbre, dynamics, rhythm, and melody.
Upper years of schooling	Utilization of musical elements in increasingly complex ways, which includes song interpretation and expressiveness, often in regard to emotional expressions in song performance

CCM vocal pedagogy has largely been guided by two main approaches. First, physiological approaches consider the mechanisms of vocal production (Hoch, 2017, 2019; Sell, 2005). A technical understanding of how sound is produced through variations in muscle movement, vocal fold tension, shapes of resonating chambers, and breath control are at the center of this approach. Second, psychoacoustic approaches focus on the acoustics of vocalizations (e.g., Fant, 1970). Research in this discipline has made important inroads into vowel modification and resonance (e.g., Coffin, 1976/1987), and the singer’s formant (e.g., Sundberg, 1989). Physiological approaches are increasingly being applied to CCM singing (e.g., Keskinen, 2013, pp. 39–42), and a more in-depth understanding of the psychoacoustics of CCM singing is developing (American Academy of Teachers of Singing, 2008, pp. 368–369; Weekly & LoVetri, 2009). However, neither approach explicitly, nor systematically, focuses on teaching expression of emotion in CCM singing performance. Consequently, specialist vocal pedagogues and generalist music teachers lack a framework for handling the “idiosyncratic and non-standardised” forms of emotional expression common in CCM.

This lack of pedagogical framework is mirrored by a paucity of research into emotional expression via lyrics and voice in CCM song. While lyrics are studied widely in literature, music therapy, and linguistics, they are commonly examined for their literary techniques, themes, and grammatical features rather than the expression of emotion (Ashtiani & Derakhshesh, 2015; Mantra & Widiastuti, 2018; O’Callaghan & Grocke, 2009; Pettijohn & Sacco, 2009). Similarly, research about emotional expression in the singing voice has primarily focused on mechanisms of production, acoustic features of a certain emotion that a singer intends to express, and the potential of individual voice qualities in contributing to the expression of certain emotions (e.g., Heidemann, 2016). To date, no method exists that examines how voice qualities are manipulated and combined to deliberately create an intended emotional effect. This lack of research is problematic. Work is needed to help students meet music curriculum outcomes and to equip CCM vocal teachers with resources to teach emotion expression in a systematic way which also captures the “non-standardised” vocal expressions. For this to occur, teachers need to be equipped with in-depth content knowledge about emotion expression in both voice quality and lyrics.

This article proposes a Systemic Functional Semiotics (SFS) perspective (Ngo et al., 2021) for examining affective meanings in both voice quality and lyrics in CCM song performance. The application is focused on upper secondary music education for both classroom-based teachers and CCM vocal teachers.¹ In this article, the term “Affect” has been adopted from the Systemic Functional Linguistics (SFL) Appraisal Framework (Martin, 2017; Martin & White, 2005) to refer to the evaluative meaning potential of language. Here, Affect refers to different kinds of emotions that language can express. We similarly use Affect to refer to the meaning potential of voice quality to express different kinds of emotions. The article begins with a review of research on emotional expression in lyrics and voice qualities, followed by the presentation of the theoretical framework. Next, an analysis is conducted of the lyrics and sung voice of the song “Someone Like You” performed by Adele Adkins, commonly known as Adele (Adkins & Wilson, 2011, track 11). This song was chosen due to its strong appeal to a wide audience (The Official UK Charts Company, 2020), its familiarity to many students and teachers, and its representativeness of a popular vocal song in terms of style, structure, and organization. It should be noted that, although this article focuses on CCM specifically, the framework can be applied to a wide range of popular music (see, for example, Spreadborough, 2018, in press). The article concludes with a discussion of the implications of this approach for teaching emotional expression in CCM songs in the upper years of schooling.

Review of related research

Research in emotional expression in lyrics

To express emotion or personal feeling in singing performance, singers must understand the meaning of the lyrics. The study of lyrics has attracted the interest of researchers from a range of fields (see Table 2). Such research demonstrates the connection between music education and Language Arts education in school curricula. However, these findings have not translated into methods for teaching songs as they do not address emotional expression through lyrics.

Table 2.

Common Approaches to Analyzing Lyrics.

Field of study	Focus of analysis	Example	Research examples
Linguistics References Socio-cultural study	Linguistic Features	Phonological Grammatical Pragmatic features	Ashtiani & Derakhshesh (2015) Eckstein (2010) Logan et al. (2004) Mantra & Widiastuti (2018) O’Callaghan & Grocke (2009) Pettijohn & Sacco (2009) Waddelow & Taul (2016)
Linguistics References Socio-cultural study	Themes Literary devices	Symbolism Imagery Metaphor

Extensive research into emotion in songs has been carried out through Music and Multimedia Information Retrieval (MMIR), which focuses on “mood trajectories” in lyrics and other acoustic features (Hu et al., 2009; Nanayakkara & Caldera, 2016; Nishikawa et al., 2011; Yang & Lee, 2009). The most common analytical method for lyrics is “probabilistic latent semantic analysis” (PLSA), an intuitive approach based on “impression” and clustering of affective words into “types” of feelings (Nanayakkara & Caldera, 2016; Nishikawa et al., 2011, p. 51). This approach detects emotions explicitly expressed as emotive words. In language, however, sophisticated expression of emotion can also be communicated implicitly using linguistic resources beyond individual emotive words, such as through figurative language (e.g., “I can’t take my eyes off you”). In such cases emotions can be strongly inferred based on co-text and context (Martin & White, 2005). As the PLSA approach in MMIR research only detects emotive words, it overlooks emotions that are expressed implicitly. Alternatively, the Appraisal Framework—an SFL description of affective meaning potential in language (Martin, 2017; Martin & White, 2005; Ngo & Unsworth, 2015)—facilitates analyses of emotions at both explicit and implicit levels. However, its application to lyrics analysis is underexplored.

Research in emotional expression in the sung voice

In music studies, emotional expression in the sung voice has been explored only marginally compared with emotional expression in instrumental music (see, for example, the work of and Juslin and Sloboda (2010)). Within such research, emotional expression has been examined with regard to mechanisms of sound production, acoustic features, and social semiosis. Research into mechanisms of sound production suggests that emotion in the voice may be understood through a process of contextualizing such sounds within one’s body (Cox, 2011). For example, air passing through incompletely tensed vocal folds along with a slight inward drop of the pharynx walls creates a breathy sound, which can be associated with low arousal emotional states, such as relaxation (Heidemann, 2016, para. 4.4). Acoustics studies concerning emotional expression in the sung voice are largely related to opera singing (Parada-Cabaleiro et al., 2018; Scherer et al., 2013; Siegwart & Scherer, 1995), and measure features such as fundamental frequency, pitch, and intensity in opera singers’ expressions of basic emotions (i.e., joy, sadness, anger, fear, and disgust). Finally, social semiotics, which considers how voice quality may be understood through an ecological and phenomenological lens, takes into account the listener’s experience of perceiving and producing sounds (e.g., Clarke, 2005; Lacasse, 2000; Reed, 2005; Tagg, 2000; Tagg & Clarida, 2003). For example, a resonant voice (especially when enhanced through processing techniques such as reverberation) can signify space, and a lack of resonance may signify closeness and intimacy (Lacasse, 2000, p. 240).

Although the above-mentioned approaches are helpful for understanding the physiological processes, acoustic features, and meaning potential of individual voice qualities, they are not always best placed to teach emotional expression in CCM singing performance. Rather, teaching emotional expression requires a comprehensive and explicit description of how voice qualities could be manipulated to express an intended emotion, to move beyond the popular pedagogical strategy of “felt emotions” (Clark & Clark, 2002). This need can be fulfilled using the Systemic Functional Semiosis approach, explained in section “An SFS perspective for understanding affective meanings in lyrics and the sung voice” of this article.

An SFS perspective for understanding affective meanings in lyrics and the sung voice

The Appraisal Framework for understanding affective meanings in lyrics

This article applies the Appraisal Framework (Martin & White, 2005) to the analysis of emotional expression, hereafter referred to as affective meaning, in lyrics. Affective meanings in language refer to different types of emotions that language has the affordances to represent. They lie within the central system of attitude, which describes linguistic resources for expressing three types of attitudinal meanings (Martin & White, 2005):

Personal feelings or affective meanings (affect).

Judgment of people’s characters and behaviors (judgment).

Appreciation of things and phenomena (appreciation).

Judgment and appreciation are both closely related to affect, invoking positive or negative affect associated with positive or negative judgment of people’s personalities and behaviors or appreciation of things. For instance, if a person judges someone else as “a coward,” the feeling associated with the negative judgment cannot be positive. In contrast, if someone evaluates their past as “glory days,” the feeling associated with the positive appreciation cannot be negative.

The affect system (a sub-system of the attitude system described above) organizes choices of affective meanings in four categories of Dis/Inclination, Un/Happiness, In/Security, and Dis/Satisfaction (see Figure 1). Each of the categories consists of more delicate, polarized positive or negative choices of contrasting feelings. For example, in the Un/Happiness category, there is a contrast between Happiness—the positive—versus Unhappiness—the negative. Even more delicately, in each polarized category, affective meanings are directed either toward one’s self or toward others. For example, within Unhappiness, there are two more delicate categories: “Antipathy” is a category of Unhappiness directed at others, while “Misery” refers to personal mood which is not necessarily directed toward any others. The affect² system and its explicit lexical realizations are presented in Figure 1.

Figure 1.

Language affect (Martin, 2017).

The “volume” of attitude generally, and affective meanings in particular, can be “turned up” (e.g., “I’m feeling so sad”) and “turned down” (e.g., “I’m a bit sad”) by graduation resources (Hood, 2010; Martin & White, 2005). Linguistic resources for graduating attitude include (a) “Isolation,” (b) “Infusion,” and (c) “Repetition,” detailed and illustrated in Figure 2.

Figure 2.

Graduation Resources for Adjusting Volumes of Attitudinal Meanings.

Affective meanings can be expressed explicitly via lexical resources or implicitly via different strategies in accordance with different degrees of implicitness. The spectrum of affective meaning from explicitness to implicitness and corresponding realization strategies are presented in Figure 3.

Figure 3.

Spectrum of Affective Meaning Explicitness/Implicitness. The Top Boxes Contain Examples From the Lyrics of “Top of the World” by The Carpenters.

The highest level of implicitness (i.e., Afford) is applied when the songwriter chooses to tell selected “stories” about their characters’ feelings without using any affective words, as exemplified in the extract from the lyrics of “Top of the World” by The Carpenters (Figure 3). To understand the affective meanings realized by Afford, the songwriter and audience need to have a shared understanding of contexts, values, or beliefs. The less implicit level of affective meaning (i.e., Flag) is realized by “strong” words or intensifiers (e.g., “top”), the same resource for upscaling or downscaling attitudinal meanings in the system of graduation. The lowest level of implicitness (i.e., Provoke) is realized by figurative language such as similes or metaphors, as the imagery created in this strategy leaves very little room for interpretation (e.g., “fire” relates to strong, burning affection). Using the Appraisal Framework, readers of lyrics can comprehend multidimensional affective meaning, such as types of affective meaning, explicitness, and graduation degrees.

In this study, the analysis of affective meanings in the lyrics of “Someone Like You” follows the “bottom up” approach, in which analysis starts with realizations of affective meanings explicitly expressed at word level or implicitly beyond word level, before working back to the “mood” of a whole section, and then of a whole song. The UAM corpus tool (O’Donnell, 2008) was employed for managing the coding during this study.

SFS framework for understanding affective meanings in the sung voice

When developing an SFS framework for emotional expression in the sung voice, it is useful to consider the SFS framework for the speaking voice (see Figure 4). The choices of affective meaning potential (or affective meaning signified) presented in Figure 4 were developed from Ekman’s five “basic emotions” (i.e., joy, sadness, fear, anger, and surprise). The semiotic voice quality realizations of each emotion were influenced by acoustics studies (Johnstone & Scherer, 2000) and social semiotics research (van Leeuwen, 1999) in emotional speaking voice. The theorization of affective meanings in the speaking voice was also an outcome of perceptive analysis of speaking voice samples. There is a correspondence between each voice quality parameter and musical elements commonly referred to in music curricula worldwide. This overlap is presented in Table 3.

Figure 4.

Voice affect Network for Spoken Voice (Ngo et al., 2021).

Table 3.

Mapping of the Voice Quality Parameters to the Musical Elements Commonly Referred to in Music Curricula Worldwide.

Musical element	Voice quality parameter
Musical element	Breathy/clear	Rough/smooth	Nasal/no-nasal	Tense/lax	Vibrato/plain	High/low	Wide/narrow	Loud/soft	Fast/slow	Short/long
Timbre	x	x	x	x	x
Pitch					x	x	x
Dynamic								x
Tempo									x
Rhythm										x

Comparisons between singing and speaking can be drawn, as they share many similar acoustic patterns (Scherer et al., 2013). In addition, both share the basic semiotic principles of voice quality parameters (van Leeuwen, 1999) of which certain combinations of different vocal features have the potential to realize certain types of emotions. However, there are several key points that need to be addressed before developing the SFS framework for sung voice. First, we cannot completely adopt the framework for speaking voice in the sung word context, because singing is heavily mediated by “constraints imposed by the musical structure” (Scherer et al., 2013, p. 218), such as rhythm and pitch. In addition, there is debate over which emotional models should be used in music studies. Much empirical research in music has examined the “utilitarian” approach to emotions based on the “five basic emotions” and dimensional models of emotion (Eerola & Vuoskoski, 2011; for earlier discussions of arousal and valance see Russell, 1980, 1991). Along with the “utilitarian” approach to emotions, there is also an established history of considering emotions induced by music from the perspective of aesthetic emotions taxonomized as, for example, wonder, transcendence, tenderness, nostalgia, peacefulness, power, joy, activation, tension, and sadness (Zentner et al., 2008). Attempts to include aesthetic emotions in empirical research have not been as widely adopted due to the challenges of defining a taxonomy of aesthetic emotions (Zentner et al., 2008).

In this article, we chose to orientate our SFS framework for sung voice toward the more utilitarian models of emotion for three reasons. First, sung voice quality presents a unique case for music and emotion studies. It is musical, as it is an integral part of song, but it is also tied closely to listeners’ lived experiences of sound. Empirical research into music and emotion, and work on aesthetic emotions of music, tend to look at instrumental music rather than sung voice quality specifically. For this reason, there is little research that examines emotions in sung voice quality, nor research into aesthetic emotions which consider the ecological context of voice quality. Therefore, we consider emotions in sung voice quality through the lens of listeners’ lived experience. This is reflected in the use of the voice quality framework for spoken voice. However, we also recognize that emotions in music may not be so “clear cut” as in spoken voice, as singing is mediated by musical elements.

Second, the voice affect network is based on basic emotions—supposedly universal emotions that have arisen through evolutionary processes (Eerola & Vuoskoski, 2013, p. 310). Emotions in song may not be so neatly classified due to the blending of emotions in music and the structural constraints mentioned above (Spreadborough, in press). For this reason, as will be seen below, combinations of voice quality parameters cannot be neatly assigned to certain emotions. Instead, emotion is classified based on “fuzzy” combinations of voice quality parameters, as is the case in emotional prototypes theory (Russell, 1980, 1991).

Third, the sung voice adaption realizes emotions through more flexible combinations of voice quality parameters. The music psychology literature has identified five categories of emotions that tend to be consistently identified by listeners, and thus are taken as the basic categories for this framework: happiness, sadness, tenderness, anger, and fear (Juslin & Laukka, 2003; Spreadborough & Anton-Mendez, 2019). The mapping of these categories to those in the voice affect network can be seen in Table 4. In this way, this study aligns with an increasing body of work in affective methodologies. Knudsen and Stage (2015) define the affective method as “as an innovative strategy for (1) asking research questions and formulating research agendas relating to affective processes, for (2) collecting or producing embodied data and for (3) making sense of this data in order to produce academic knowledge” (p. 1). In this article, we apply empirical research to the embodied experience (through SFS) and use this as a basis of analysis to produce academic knowledge.

Table 4.

Mapping of Discrete Emotions to the SFS Voice affect Network.

Emotion categories used for sung voice analysis (based on Juslin & Laukka, 2003; Spreadborough & Anton-Mendez, 2019)	Happiness	Tenderness	Sadness	Anger	Fear
Affective meanings in Ngo et al. (2021) voice affect network	Cheer	Affection	Misery	Anger	Fear

SFS: Systemic Functional Semiotics.

Figure 5 outlines the parameters (drawn from Ngo et al., 2021; van Leeuwen, 1999) for realizing emotions in the sung voice. Vocal features of fast/slow, loud/soft, high/low, and tense/lax are robust markers of specific emotions (Juslin & Laukka, 2003, p. 802). Rough/smooth, breathy/clear, nasal/non-nasal, and vibrato/plain have been assigned a level on the graduation scale in Figure 5 as they also signify emotions. However, these parameters are more context-dependent and this should be considered when assessing affective meaning.³ For example, fast vibrato may be related to anger, fear, and cheer; medium vibrato may be related to affection; while slow vibrato may be related to misery (Juslin & Laukka, 2003, p. 797). Wide/narrow and short/long contribute to affective meaning in the spoken voice; however, the relationship between this and musical expression is not clear (see discussion of wide/narrow in Juslin & Laukka, 2003). Thus, wide/narrow and short/long have not been assigned a level on the graduation scale in Figure 5, although they have been retained as they are voice quality parameters nonetheless. Drawing on previous work by Spreadborough (2018) on applying voice quality parameters to annotate and analyze sung voice quality, the model for sung voice presented in Figure 5 promotes the assessment of affective meanings by the degree of graduation of each feature. This approach is similar to that of graduation in the volume of attitudes using linguistics resources (e.g., so shy, a bit shy). The voice functions in a similar manner by graduating voice quality parameters on a bipolar scale. For example, tension may be graduated from very lax, in which the sound is completely relaxed and low in pitch, to very tense, in which the sound is very strained and high in pitch. The facility for graduating these voice parameters is shown in Figure 5. While Figure 5 shows how emotions may be expressed through certain combinations of parameters, there is room for variation within such expression. In other words, the combinations present in Figure 5 are “fuzzy” prototypes of emotion categories, not clear-cut definitions that must be strictly observed.

Figure 5.

Combinations of Voice Quality Parameters for Expressing Different Categories of Emotion in the Sung Voice.

A bottom-up approach is used when analyzing affective meaning via voice quality, like the lyric analysis method above. The analysis begins at the level of the individual voice quality of each note. This assessment is then used to guide the determination of the affective meaning for the phrase. Note here that voice quality parameters are not examined for every vocalization expressed within a phrase. Rather, the voice quality parameters are only examined for the salient musical notes (i.e., notes which are most representative of emotions, as determined by the graduation of the vocal parameters shown in Figure 5 within each phrase). By retaining some fluidity between the phrase level and note level, it is possible to manage the rate of change during analysis (i.e., not changing affect judgment at every note), while also accounting for those notes within the phrase that contribute most to affect. This process is not unlike that used in the study of melody, for example, in which some notes are recognized as being more integral to the construction of that melody than others (e.g., the most structurally prominent notes). Moving from the micro- to the macro-level, then, the salient note colors the phrase, which then colors the section, and in turn the song.

When analyzing emotions in voice qualities in the sung voice, we propose assessing vocal features against a singer’s “baseline” voice quality within a song. This is because the vocal features in Figure 5 are not fixed points. For example, there is no absolute value for an extremely breathy voice. Rather, each singer or song has a different point of equipoise between extremes of emotion. This equipoise represents the voice quality baseline for the song and should be identified prior to analysis so that other vocal features may be assessed against this point.

Intersemiosis: Voice quality and language

From the SFS perspective, a singing performance is considered a multimodal text consisting of multiple meaning-making resources. These resources include language (i.e., lyrics), sound (e.g., musical notes and instrumentation), paralanguage (e.g., voice quality, facial expression, and body language), and costume and setting. Meaning realized by one semiotic resource may have convergent or divergent relationships with another resource (Ngo, 2018; Painter et al., 2013).

The convergent inter-semiotic relationship occurs when language and voice quality create similar affective meanings. For example, if the lyrics “I hate to turn up” express a negative feeling and the voice qualities over this phrase also express a negative feeling, then the two resources create a convergent relationship. In this case, both resources together create semantic redundancy, strengthening the overall meaning. In contrast, if over the same lyrics, the voice qualities express an opposite feeling, then the two modes have a divergent relationship. A divergent relationship indicates the existence of meaning tension, and the context of the song may help interpret the story behind such tension.

Data analysis: Affective meanings in lyrics and sung voice

The Appraisal Framework (Martin, 2017; Martin & White, 2005) and the voice affect adapted for this study were employed to examine affective meanings in “Someone Like You” performed by Adele. Figure 6 provides a timeline and lyric structure for the song “Someone Like You,” together with the coding of the lyrics structure.

Figure 6.

Timeline of “Someone Like You.”

Affective meaning in “Someone Like You” lyrics

Prior to analyzing affective meaning throughout the whole song’s lyrics using the Appraisal Framework, an inter-rater reliability exercise was conducted. A trained linguist was invited to code the first two verses of the song using the Appraisal Framework. Their coding was later compared with that of one of the authors. The inter-rater reliability rate was 98%. The discrepancy in the two versions of the coding was discussed and the solution was applied during the author’s analysis of the rest of the data.

The analysis of types and graduation of affective meanings indicates that the whole song is subtly colored by the negative feelings of [Unhappiness: Misery] and [Unhappiness: Antipathy], although there are instances of brighter moods of the singer expressed by positive expressions of [Inclination: Desire]. However, these upbeat feelings are often triggered by memories of the past, which are no longer present. Examples of the results of analysis of affective meanings in verse 1, pre-chorus, and chorus are presented in Tables 5 to 7, respectively, (“+ve” and “−ve” are used for coding positive and negative, respectively).

Table 5.

Affective Meanings in Verse 1.

Types of affect	Expressions		Trigger	Graduation
Types of affect	Explicit	Implicit	Trigger	Graduation
−ve Unhappiness:Misery[toward self]		Afford: I heard that you’re settled downThat you found someone and you’re married now.	Ex lover’s current marital status	N/A
−ve Unhappiness: Misery[toward self]		Afford: Guess she gave you things I didn’t give to you.	Ex lover’s new partner	N/A
−ve Dissatisfaction:Displeasure[toward ex-lover]		Afford: Old friend, why are you so shy?Ain’t like you to hold back or hide from the light.	–ve Judgment of ex lover’s tenacity	N/A

Table 6.

Affective Meanings in the Pre-Chorus.

Types of affect	Expressions		Trigger	Graduation
Types of affect	Explicit	Implicit	Trigger	Graduation
−veUnhappiness: Antipathy[toward self]	hate[I hate to turn up out of the blue uninvited]		−ve Judgment (implicit) of her behavior [turn up out of the blue uninvited] to be present to her ex-lover	N/A
−veUnhappiness: Antipathy[toward self]		Afford: But I couldn’t stay away, I couldn’t fight it.	−ve Judgment (explicit) of her capacity to restrain herself	Upscale:Repetition
+veInclination: Desire	hoped[I had hoped you’d see my face and that you’d be reminded that for me it isn’t over].		Irrealis trigger:her ex-lover to see her and remember that she still has the same feelings for him.	N/A
−veUnhappiness:Misery	-o	Afford for me it isn’t over	Her lingering loving feeling toward her ex-lover

Table 7.

Affective Meanings in the Choruses.

Types of affect	Expressions		Trigger	Graduation
Types of affect	Explicit	Implicit	Trigger	Graduation
+veSecurity: Confidence		Afford: Never mind, I’ll find someone like you	The possibility to be able to move on
+veInclination: Desire	wish[I wish nothing but the best for you too]		Irrealis:[nothing but the best for you]	Upscale:[nothing but the best]
+veInclination: Desire	beg[Don’t forget me, I beg]		Irrealis:[don’t forget me]	Upscale:[beg]
−veUnhappiness: Misery		Afford: +ve and −ve Appreciation:“Sometimes it lasts in love but sometimes it hurts instead.”“Sometimes it lasts in love but sometimes it hurts instead.”	The nature of a loving relationship

The function of each verse is to set up the situation for the expression of feelings in the chorus. The lyrics in verse 1 do this by telling the audience about the character’s broken relationship and that her ex-lover has already moved on to another relationship. The first sentence invokes empathy for the feeling of sadness [Unhappiness: Misery] using the implicit strategy, Afford. This is used to express the negative feeling of [Dissatisfaction: Displeasure] associated with the character “I” in their explicit, negative, and intensified judgment of their ex-lover’s lack of confidence for hiding away via use of the intensifier “so” (i.e., “Old friend, why are you so shy”).

The character’s negative feeling continues in the pre-chorus, with an explicit expression of [Unhappiness: Antipathy] via the emotive word “hate” directed at the “I” character herself for her own uncontrolled behavior. The negative self-judgment becomes explicit in the following line via the repetition of “couldn’t,” indicating her inability to restrain her emotions and upscaling her negative attitude toward herself. The “inappropriate” behavior is also explained by her feeling of [Inclination: Desire], expressed explicitly via the emotive word “hope” for her ex-lover to know about the lingering feeling she still has for him. The listener empathizes with the hopeless feeling the character is experiencing because up to this point in the song, the first hint of positive feeling—[Inclination: Desire]—is triggered by an unreal prospect. In direct comparison with narrative structure, the pre-chorus acts as the Complication stage with the last sentence representing the Climax. The Complication reaches its highest point, urging a resolution which will unfold in the chorus. This aligns with the function of the pre-chorus, which creates anticipation and builds up to the chorus.

The chorus unveils the resolution for emotional conflict built in the pre-chorus. The linguistic analysis of this section reveals an upbeat emotion starting with a positive feeling of [Security: Confidence] triggered by the prospect that the character would be able to move on. The expression of this feeling, however, is the highest level of implicitness—Afford. The use of the abbreviation of future tense expression, “I’ll find,” instead of the full form, “I will find,” together with a description of the next person the character will find as being “like you,” indicate a very weak level of confidence. Thus, although these instances of feeling are positive, they are also triggered by imagined prospects. This line of the section has a mixture of positive and negative Appreciation of the nature of a loving relationship. However, as the negative Appreciation emotion (i.e., “but sometimes it hurts instead”) is placed at the end of the section, it colors the whole chorus with feelings of negativity.

In sum, the Appraisal Framework enables a comprehensive, multidimensional understanding of affective meanings and their explicit and implicit expressions. This understanding is constructed in close relation to the function of each section of popular ballad songs. This conceptual (and analytical) framework demonstrates significant advantages over other analytical frameworks for analyzing affective meanings in lyrics (such as the probabilistic latent semantic analysis), as the same theoretical perspective can be adopted to analyze affective meanings in sung voice quality.

Affective meaning in “Someone Like You” sung voice

This section demonstrates the application of the voice affect framework for understanding affective meaning realized by voice quality in verse 1, pre-chorus 1, and chorus 4. Inter-rater reliability exercises for voice quality judgments were conducted throughout the analysis. One author (a music specialist) first analyzed the voice quality and annotated the features on the graduation scales shown in Figure 5. The other author (a social semiotics specialist) then cross-checked these annotations against their own. The annotations of the two authors were highly aligned. Any discrepancy in the two annotations was managed by robust discussion of the ratings until agreement was reached.

The vocal features were analyzed against Adele’s baseline voice quality in the song. This baseline is identified as Very Mild Sadness as found in the first half of pre-chorus 1. Analysis results indicate that the whole song is characterized by Moderate to Extreme feelings of Sadness (Figure 7).

Figure 7.

High-Level Summary of the Emotional Expression in Each of the Sections of “Someone Like You.”

Flashes of Fear color aspects of the voice quality; however, these are short-lived emotions which are blended with different levels of Sadness. Figures 8 to 10 show each of the vocal features on a bipolar, graduated scale for each of the sections analyzed.

Figure 8.

The Graduation of Vocal Parameters in Verse 1.

Figure 9.

The Graduation of Vocal Parameters in Pre-Chorus 1.

Figure 10.

The Graduation of Vocal Parameters in Chorus 4.

Verse 1 represents Mild to Moderate Sadness (Figure 8). The slow delivery of the first two lines coupled with low pitch and soft dynamic suggest the character is experiencing a deep sense of listlessness and disengagement. The rougher, louder, and wider variations introduced in phrases 5, 7, 8, 9, and 10 suggest an intensification of this emotion. However, while salient, these intensifications are short-lived. The voice quality returns to the sense of Mild Sadness. The blending of this Mild Sadness with stabs of moderate intensification conveys the potency of the sadness experienced by Adele’s character.

The voice quality in pre-chorus 1 is generally representative of Very Mild Sadness (see Figure 9). The only exception to this mood is the final five words, which are more expressive of Mild Sadness. As mentioned above, this section is considered the baseline because, while the vocal parameters realize feelings of Sadness (see Figure 5), as in verse 1 these expressions are not extreme. For example, the voice is soft, but it is not at the extreme end of the soft parameter that is achieved in this song, as is evident in the extreme soft in the last phrase “for me, it isn’t over.” Thus, the mood created in pre-chorus 1 is that of thinly veiled control—the voice quality is not a happy one, but it is also not overrun with intense emotion.

While pre-chorus 1 represents the baseline, and verse 1 Mild to Moderate Sadness, chorus 4 represents Extreme to Very Extreme Sadness (see Figure 10). The voice quality of chorus 4 is almost exclusively loud, wide, and high. Roughness, tenseness, vibrato, breathiness, and nasality exhibit some variability, but generally tend toward the extreme of the scale. These combinations of vocal features typify Extreme to Very Extreme Sadness. The exception to this is in phrase 6 where Adele almost screams the words “I beg.” The very extreme roughness, breathiness, and tension delivered at the top of the singer’s register, but with markedly less vibrato, suggest Very Extreme Anger. This serves to escalate the feeling of sadness, intensifying the emotion significantly and creating an overall mood of extreme sadness and pain.

In sum, the framework for understanding affective meanings in the sung voice presented in Figure 5 facilitates systematic identification of emotions and moods, as both discrete and blended entities. The success of the conceptual and analytical applications of this framework also strongly positions it for pedagogical application. The interaction between lyrics and voice in constructing affective meaning in the song is examined in section “Intersemiosis: Interaction between lyrics and voice qualities.”

Intersemiosis: Interaction between lyrics and voice qualities

The interplay between lyrics and voice qualities in “Someone Like You” effectively conveys the character’s interior struggle between the heart and the mind. Figures 11 to 13 present the interactions between language and voice qualities in verse 1, pre-chorus 1, and chorus 4. The types and degrees of feelings realized in the two modes are concurrently represented in the same “stave,” with the top half for positive emotions and the bottom half for negative emotions. The coding scheme is also provided within these same figures.

Figure 11.

The Interaction Between Lyrics and Voice Quality in Verse 1.

Figure 12.

The Interaction Between Lyrics and Voice Quality in Pre-Chorus 1.

Figure 13.

The Interaction Between Lyrics and Voice Quality in Chorus 4.

In the first line of the first verse, the two modes (lyrics and voice quality) converge in representing a mild level of [Misery] and Sadness. This aligns with the function of the first verse—to set up the story by focusing on the narrative rather than emotional expression. However, as the verse unfolds, the singer takes us further into her character’s interior world, a world in which turmoil is revealed through a divergent intersemiotic relationship. The lyrics and voice quality diverge both in kind of emotion ([Misery] vs. Anger) and emotional intensity (Moderate to Mild) (see Figure 11).

In pre-chorus 1, the two modes become increasingly divergent in representing the character’s feelings. This divergence, however, is not the result of different degrees of negative emotions between lyrics and voice quality. Rather, it is the result of opposing polarities of positive-negative feelings. That is, while the lyrics move from a strong negative feeling of “hatefulness” in verse 1 to a positive feeling of “hopefulness” in the pre-chorus, the voice qualities remain consistently negative in the expression of Sadness. It is at this point that the character’s interior presents itself—the goal of this encounter is not to obtain closure, but an attempt to win back the ex-lover. This is well aligned with the pre-chorus’s function of creating anticipation in the build-up to the chorus, a place where the listener expects a resolution to this conflict (see Figure 12).

Chorus 4 functions as the emotional heart of the tale in which the character’s emotional resolution is unveiled. Here the divergent relationship between emotions expressed via lyrics and voice quality becomes increasingly obvious. This is vividly shown in the visual representation in the first two lines of the chorus, where a void of ambiguity emerges as the positive, lyrical message contrasts starkly with the negativity of the voice quality (see Figure 13). That is, while mild to moderate hints of positive emotions are realized in language, voice qualities move further toward the most extreme degree of Sadness. The chorus ends with moderate [Misery] in the lyrics but extreme Sadness in the voice. By playing on the divergence of lyric-voice quality in the first two stanzas (i.e., over the lyrics “Never mind, I’ll find someone like you, I wish nothing but the best for you too. Don’t forget me, I beg, I’ll remember you said”), but concluding the song with a more aligned sense of misery, Adele’s overall performance successfully constructs an atmosphere of pain and hopelessness. The listener empathizes with the character, understanding that the more the character is aware of the reality of her situation, the more miserable she feels.

The examination of the intersemiosis between lyrics and voice qualities in the sung voice demonstrates the expressive and creative power of the singer. The power of Adele’s performance is rooted in her ability to express simultaneous, yet very distinct, emotions through lyrics and voice quality. To achieve this, one must understand affective meaning potential both in the lyrics and in the sung voice. It is only when one understands these meaning potentials that one can manipulate these resources to create convergent or divergent intersemiosis in the expression and creation of narrative.

Conclusion: Discussion and implications for music education

Discussion

This article has discussed an SFS approach to understanding emotional expression in singing performance, beginning by explaining how affective meanings in lyrics and voice qualities can be understood independently, before considering how they relate through intersemiosis. This article has demonstrated the advantages of the employment of the Appraisal Framework (Martin & White, 2005) for analyzing affective meaning in lyrics over other methods discussed in section “Research in emotional expression in lyrics.” The Appraisal Framework is advantageous as it facilitates the comprehensive detection of both implicit and explicit emotional expressions in lyrics in a systematic fashion.

Similarly, the SFS approach to analyzing emotional expressions in vocal qualities offers a systematic method for describing characteristics of the voice and discussing their implications for emotional expression. Future research needs to more closely examine how voice quality in song relates to utilitarian (discrete/dimensional) and aesthetic models of emotion. In this context, the voice presents a unique challenge as it is both a musical device (regulated by other musical features such as rhythm and pitch) and is related to listeners’ everyday lived experiences. In using SFS for both lyrics and voice, the conceptual framework of intersemiosis can be applied to understand the convergent/divergent relationships between lyrics and voice quality—something that no other theoretical frameworks have formally discussed.

Implications for music education

The SFS approach in music research has significant implications for music education as it contributes to available resources for understanding, performing, and creating song—cornerstones of music curricula worldwide. In addition, this framework does not require specialized musical knowledge to be applied to understanding song, and is therefore an accessible resource for all classroom-based music teachers.⁴ CCM vocal teachers can also apply this approach to teaching expression in song in a systematic and structured way. This can be achieved by teachers using the SFS model of emotive voice quality to explain how each emotion can be expressed via manupulating each voice quality parameter. Thus, this framework has the potential to enhance students’ expressive competence for both critiquing and creating song.

The SFS approach presented in this article also has the potential to strengthen the connection between music and literacy education. As discussed in the introduction, the beneficial relationship between music learning and literacy is well documented. Thus, teaching students to understand the delicacy of emotional expression in lyrics both develops their capacity for musical expressivity and enhances their understanding of language in general and how to use it effectively in different contexts (Hansen et al., 2014). Improving students’ musical expressivity in this way has clear benefits for strengthening their critical literacy and higher order interpretive comprehension skills, which is applicable to all other subjects (Lloyd, 2003). It also provides a shared language for students and educators to discuss emotional communication in the voice (both spoken and sung). Furthermore, enhancing students’ capacity to detect and decipher emotional cues in musical contexts has implications for everyday communication. For instance, practicing deeper and more nuanced levels of understanding and expression in the sung voice may develop students’ capacities to be more effective communicators in their spoken word contexts and provide resources for students to critically evaluate the paralinguistic messages of others.

In sum, the goal of curricula worldwide is to educate the whole person. Central to this is the development of capacity for understanding and expression. Music is well placed to meet this need, especially as music curricula already prioritize aspects of understanding and expression. Thus, through providing a framework to optimize the understanding and expression of emotion in vocal songs, this article offers a novel and impactful resource for educating the whole person through musical engagement.

Footnotes

The Perspectives Series is a scholarly forum for authors to present ideas and perspectives in music education. Perspectives may seek to engender debate from a personal values position or stake a claim on a new methodological, philosophical or pragmatic ‘space’.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Thu Ngo

Notes

Author biographies

Thu Ngo is a lecturer in language and literacy education at the Australian Catholic University. Her current research is in the field of systemic functional semiotics and linguistics. She is interested in the attitudinal meanings in language and semiotics of body language, facial expressions, and voice quality. Her recent publications are in the film studies and multimodal literature/literacy education.

Kristal Spreadborough is an academic specialist with the Melbourne Data Analytics Platform, University of Melbourne. Her research areas include music, psychology, semiotics, data analytics and digital and data ethics. Her research interests lie at the intersection of these areas. She has recently produced work on voice quality in popular songs, historical understandings of listening practices, methods for data led research, and data ethics.

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