The number of older adults is growing significantly worldwide. At the same time, technological developments are rapidly evolving, and older populations are expected to interact more frequently with such sophisticated systems. Automated speech recognition (ASR) systems is an example of one technology that is increasingly present in daily life. However, age-related physical changes may alter speech production and limit the effectiveness of ASR systems for older individuals. The goal of this paper was to summarize the current knowledge on ASR systems and older adults. The PRISMA method was employed and 17 studies were compared on the basis of word error rate (WER). Overall, WER was found to be influenced by age, gender, and the number of speech samples used to train ASR systems. This work has implications for the development of future human-machine technologies that will be used by a wide range of age groups.
References
1.
BabaA.YoshizawaS.YamadaM.LeeA.ShikanoK. (2001). Elderly acoustic model for large vocabulary continuous speech recognition. In Seventh European Conference on Speech Communication and Technology.
2.
AndersonS.LibermanN.BernsteinE.FosterS.CateE.LevinB.HudsonR. (1999). Recognition of elderly speech and voice-driven document retrieval. In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings ICASSP99 (pp. 145-148). March.
3.
YatoF.InoueN.HashimotoK. (1999). A study of Speech recognition for the Elderly. In Sixth European Conference on Speech Communication and Technology.
4.
WilponJ. G.JacobsenC. N. (1996). A study of speech recognition for children and the elderly. In 1996 IEEE international conference on acoustics, speech, and signal processing conference proceedings (pp. 349-352). May.
5.
VipperlaR.RenalsS.FrankelJ. (2008). Longitudinal study of ASR performance on ageing voices. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 2550–2553).
6.
AmanF.VacherM.RossatoS.PortetF. (2013). Speech recognition of aged voice in the AAL context: Detection of distress sentences. In 2013 7th Conference on Speech Technology and Human - Computer Dialogue, SpeD 2013 (pp. 1–8). IEEE.
7.
BabaA.YoshizawaS.YamadaM.LeeA.ShikanoK. (2004). Acoustic models of the elderly for large-vocabulary continuous speech recognition. Electronics and Communications in Japan (Part II: Electronics), 87(7), 49-57.
8.
VacherM.AmanF.RossatoS.PortetF. (2015). Development of automatic speech recognition techniques for elderly home support: Applications and Challenges. In International Conference on Human Aspects of IT for the Aged Population (pp. 341-353). August.
9.
RavichanderV.SteveR.JoeF. (2010). Ageing Voices: The Effect of Changes in Voice Parameters on ASR Performance. EURASIP Journal on Audio, Speech, and Music Processing, 2010.
10.
HämäläinenA.TeixeiraA.AlmeidaN.MeinedoH.FegyóT.DiasM. S. (2015). Multilingual speech recognition for the elderly: The AALFred personal life assistant. Procedia Computer Science, 67, 283-292.
11.
KalaskyM. A.CzajaS. J.SharitJ.NairS. N. (1999). Is Speech Recognition Technology Robust for Older Populations? In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 43(2), 123-127. Los Angeles, CA. September.
12.
AmanF.AubergéV.VacherM. (2016). Influence of expressive speech on ASR performances: application to elderly assistance in smart home. In International Conference on Text, Speech, and Dialogue (pp. 522-530). September.
13.
HämäläinenA.MeinedoH.TjalveM.PellegriniT.TrancosoI.DiasM. S. (2014). Improving speech recognition through automatic selection of age group–Specific Acoustic Models. In International Conference on Computational Processing of the Portuguese Language (pp. 12-23). October.
14.
AmanF.VacherM.RossatoS.PortetF. (2013, August). Analysing the performance of automatic speech recognition for ageing voice: Does it correlate with dependency level? In 4th Workshop on Speech and Language Processing for Assistive Technologies (pp. 9-15).
15.
PellegriniT.HedayatiV.TrancosoI.HämäläinenA.DiasM. S. (2014). Speaker age estimation for elderly speech recognition in European Portuguese. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 2962–2966).
16.
PellegriniT.TrancosoI.HämäläinenA.CaladoA.DiasM. S.BragaD. (2012). Impact of age in ASR for the elderly: preliminary experiments in European Portuguese. In Advances in Speech and Language Technologies for Iberian Languages (pp. 139-147). Springer, Berlin, Heidelberg.
17.
PellegriniT.HämäläinenA.de MareüilP. B.TjalveM.TrancosoI.CandeiasS.DiasM. S.BragaD. (2013). A corpus-based study of elderly and young speakers of European Portuguese: acoustic correlates and their impact on speech recognition performance. In INTERSPEECH (pp. 852-856).
18.
CzajaS.RogersW.FiskA.CharnessN.SharitJ. (2009). Designing for older adults: Principles and creative human factors approaches. CRC press.
19.
HuangX.LeeK. F. (1993). On speaker-independent, speaker-dependent, and speaker-adaptive speech recognition. IEEE Transactions on Speech and Audio processing, 1(2), 150-157.
20.
MoherDLiberatiATetzlaffJ1AltmanDG, The PRISMA Group (2009) Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097.
21.
National Research Council. (2004). Technology for Adaptive Aging. Steering Committee for the Workshop on Technology for Adaptive Aging. National Academies Press.
22.
OrtmanB. J. M.VelkoffV. a.HoganH. (2014). An aging nation: The older population in the United States. US Department of Commerce: US Census Bureau, 1–28.
23.
RavichanderV.SteveR.JoeF. (2010). Ageing Voices: The Effect of Changes in Voice Parameters on ASR Performance. EURASIP Journal on Audio, Speech, and Music Processing, 2010.
24.
VipperlaR. (2011). Automatic Speech Recognition for ageing voices.
