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Improving Speech Recognition through Automatic Selection of Age Group – Specific Acoustic Models

  • Conference paper
Computational Processing of the Portuguese Language (PROPOR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8775))

Abstract

The acoustic models used by automatic speech recognisers are usually trained with speech collected from young to middle-aged adults. As the characteristics of speech change with age, such acoustic models tend to perform poorly on children’s and elderly people’s speech. In this study, we investigate whether the automatic age group classification of speakers, together with age group –specific acoustic models, could improve automatic speech recognition performance. We train an age group classifier with an accuracy of about 95% and show that using the results of the classifier to select age group –specific acoustic models for children and the elderly leads to considerable gains in automatic speech recognition performance, as compared with using acoustic models trained with young to middle-aged adults’ speech for recognising their speech, as well.

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References

  1. Lee, S., Potamianos, A., Narayanan, S.: Acoustics of Children’s Speech: Developmental Changes of Temporal and Spectral Parameters. J. Acoust. Soc. Am. 10, 1455–1468 (1999)

    Article  Google Scholar 

  2. Huber, J.E., Stathopoulos, E.T., Curione, G.M., Ash, T.A., Johnson, K.: Formants of Children, Women and Men: The Effects of Vocal Intensity Variation. J. Acoust. Soc. Am. 106(3), 1532–1542 (1999)

    Article  Google Scholar 

  3. Xue, S., Hao, G.: Changes in the Human Vocal Tract Due to Aging and the Acoustic Correlates of Speech Production: A Pilot Study. Journal of Speech, Language, and Hearing Research 46, 689–701 (2003)

    Article  Google Scholar 

  4. Pellegrini, T., Hämäläinen, A., Boula de Mareüil, P., Tjalve, M., Trancoso, I., Candeias, S., Sales Dias, M., Braga, D.: A Corpus-Based Study of Elderly and Young Speakers of European Portuguese: Acoustic Correlates and Their Impact on Speech Recognition Performance. In: Interspeech, Lyon (2013)

    Google Scholar 

  5. Narayanan, S., Potamianos, A.: Creating Conversational Interfaces for Children. IEEE Speech Audio Process. 10(2), 65–78 (2002)

    Article  Google Scholar 

  6. Strommen, E.F., Frome, F.S.: Talking Back to Big Bird: Preschool Users and a Simple Speech Recognition System. Educ. Technol. Res. Dev. 41(1), 5–16 (1993)

    Article  Google Scholar 

  7. Anderson, S., Liberman, N., Bernstein, E., Foster, S., Cate, E., Levin, B., Hudson, R.: Recognition of Elderly Speech and Voice-Driven Document Retrieval. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, pp. 145–148 (1999)

    Google Scholar 

  8. Takahashi, S., Morimoto, T., Maeda, S., Tsuruta, N.: Dialogue Experiment for Elderly People in Home Health Care System. In: Matoušek, V., Mautner, P. (eds.) TSD 2003. LNCS (LNAI), vol. 2807, pp. 418–423. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  9. Teixeira, V., Pires, C., Pinto, F., Freitas, J., Dias, M.S., Mendes Rodrigues, E.: Towards Elderly Social Integration using a Multimodal Human-computer Interface. In: Proc. of the 2nd International Living Usability Lab Workshop on AAL Latest Solutions, Trends and Applications, AAL 2012, Milan (2012)

    Google Scholar 

  10. Wilpon, J.G., Jacobsen, C.N.: A Study of Speech Recognition for Children and Elderly. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Atlanta, GA, pp. 349–352 (1996)

    Google Scholar 

  11. Potamianos, A., Narayanan, S.: Robust Recognition of Children’s Speech. IEEE Speech Audio Process 11(6), 603–615 (2003)

    Article  Google Scholar 

  12. Hämäläinen, A., Miguel Pinto, F., Rodrigues, S., Júdice, A., Morgado Silva, S., Calado, A., Sales Dias, M.: A Multimodal Educational Game for 3-10-year-old Children: Collecting and Automatically Recognising European Portuguese Children’s Speech. In: Workshop on Speech and Language Technology in Education, Grenoble (2013)

    Google Scholar 

  13. Pellegrini, T., Trancoso, I., Hämäläinen, A., Calado, A., Sales Dias, M., Braga, D.: Impact of Age in ASR for the Elderly: Preliminary Experiments in European Portuguese. In: IberSPEECH, Madrid (2012)

    Google Scholar 

  14. Vipperla, R., Renals, S., Frankel, J.: Longitudinal Study of ASR Performance on Ageing Voices. In: Interspeech, Brisbane, pp. 2550–2553 (2008)

    Google Scholar 

  15. Batliner, A., Blomberg, M., D’Arcy, S., Elenius, D., Giuliani, D., Gerosa, M., Hacker, C., Russell, M., Steidl, S., Wong, M.: The PF_STAR Children’s Speech Corpus. In: Interspeech, Lisbon (2005)

    Google Scholar 

  16. Hämäläinen, A., Rodrigues, S., Júdice, A., Silva, S.M., Calado, A., Pinto, F.M., Dias, M.S.: The CNG Corpus of European Portuguese Children’s Speech. In: Habernal, I. (ed.) TSD 2013. LNCS (LNAI), vol. 8082, pp. 544–551. Springer, Heidelberg (2013)

    Google Scholar 

  17. Cucchiarini, C., Van Hamme, H., van Herwijnen, O., Smits, F.: JASMIN-CGN: Extension of the Spoken Dutch Corpus with Speech of Elderly People, Children and Non-natives in the Human-Machine Interaction Modality. In: Language Resources and Evaluation, Genoa (2006)

    Google Scholar 

  18. Hämäläinen, A., Pinto, F., Sales Dias, M., Júdice, A., Freitas, J., Pires, C., Teixeira, V., Calado, A., Braga, D.: The First European Portuguese Elderly Speech Corpus. In: IberSPEECH, Madrid (2012)

    Google Scholar 

  19. Hämäläinen, A., Avelar, J., Rodrigues, S., Sales Dias, M., Kolesiński, A., Fegyó, T., Nemeth, G., Csobánka, P., Lan Hing Ting, K., Hewson, D.: The EASR Corpora of European Portuguese, French, Hungarian and Polish Elderly Speech. In: Langauge Resources and Evaluation, Reykjavik (2014)

    Google Scholar 

  20. Minematsu, N., Sekiguchi, M., Hirose, K.: Automatic Estimation of One’s Age with His/Her Speech Basedupon Acoustic Modeling Techniques of Speakers. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, pp. 137–140 (2002)

    Google Scholar 

  21. Dobry, G., Hecht, R., Avigal, M., Zigel, Y.: Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on the Acoustic Speech Signal. IEEE Transactions on Audio, Speech & Language Processing 19(7), 1975–1985 (2011)

    Article  Google Scholar 

  22. Bahari, M., McLaren, M., Van Hamme, H., Van Leeuwen, D.: Age Estimation from Telephone Speech Using i-Vectors. In: Interspeech, Portland, OR (2012)

    Google Scholar 

  23. Neto, J., Martins, C., Meinedo, H., Almeida, L.: The Design of a Large Vocabulary Speech Corpus for Portuguese. In: European Conference on Speech Technology, Rhodes (1997)

    Google Scholar 

  24. Eyben, F., Wollmer, M., Schuller, B.: openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor. In: ACM International Conference on Multimedia, Florence, pp. 1459–1462 (2010)

    Google Scholar 

  25. Meinedo, H., Trancoso, I.: Age and Gender Detection in the I-DASH Project. ACM Trans. Speech Lang. Process. 7(4), 13 (2011)

    Article  Google Scholar 

  26. Schuller, B., Steidl, S., Batliner, A., Noeth, E., Vinciarelli, A., Burkhardt, F., van Son, R., Weninger, F., Eyben, F., Bocklet, T., Mohammadi, G., Weiss, B.: The Interspeech 2012 Speaker Trait Challenge. In: Interspeech 2012, Portland, OR (2012)

    Google Scholar 

  27. Weninger, F., Eyben, F., Schuller, B.W., Mortillaro, M., Scherer, K.R.: On the Acoustics of Emotion in Audio: What Speech, Music and Sound Have in Common. Frontiers in Psychology, Emotion Science, Special Issue on Expression of Emotion in Music and Vocal Communication 4(Article ID 292), 1–12 (2013)

    Google Scholar 

  28. Hall, M.: Correlation-Based Feature Subset Selection for Machine Learning. Hamilton, New Zealand (1998)

    Google Scholar 

  29. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11 (2009)

    Google Scholar 

  30. Platt, J.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning (1998)

    Google Scholar 

  31. Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13(3), 637–649 (2001)

    Article  MATH  Google Scholar 

  32. Linville, S.E.: Vocal Aging. Singular, San Diego (2001)

    Google Scholar 

  33. Microsoft Speech Platform Runtime (Version 11), http://www.microsoft.com/en-us/download/details.aspx?id=27225 (accessed March 25, 2013)

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Author information

Authors and Affiliations

  1. Microsoft Language Development Center & ISCTE, University Institute of Lisbon, Lisbon, Portugal

    Annika Hämäläinen & Miguel Sales Dias

  2. INESC-ID Lisboa, Lisbon, Portugal

    Hugo Meinedo & Isabel Trancoso

  3. Instituto Superio Técnico, Lisbon, Portugal

    Isabel Trancoso

  4. Microsoft & University of Washington, Seattle, WA, USA

    Michael Tjalve

  5. IRIT - Université Toulouse III - Paul Sabatier, Toulouse, France

    Thomas Pellegrini

Authors
  1. Annika Hämäläinen
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  2. Hugo Meinedo
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  3. Michael Tjalve
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  4. Thomas Pellegrini
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  5. Isabel Trancoso
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  6. Miguel Sales Dias
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Editor information

Editors and Affiliations

  1. FCHS, Universidade do Algarve, Campus de Gambelas,, 8005-139, Faro, Portugal

    Jorge Baptista

  2. INESC-ID Lisboa, Lisbon, Portugal

    Nuno Mamede

  3. IT-University of Coimbra, Coimbra, Portugal

    Sara Candeias

  4. USP-EACH, São Paulo-SP, Brazil

    Ivandré Paraboni

  5. USP-ICMC, Universidade de São Paulo, São Carlos, SP, Brazil

    Thiago A. S. Pardo

  6. SCC-ICMC, University of São Paulo, São Carlos, SP, Brazil

    Maria das Graças Volpe Nunes

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© 2014 Springer International Publishing Switzerland

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Hämäläinen, A., Meinedo, H., Tjalve, M., Pellegrini, T., Trancoso, I., Dias, M.S. (2014). Improving Speech Recognition through Automatic Selection of Age Group – Specific Acoustic Models. In: Baptista, J., Mamede, N., Candeias, S., Paraboni, I., Pardo, T.A.S., Volpe Nunes, M.d.G. (eds) Computational Processing of the Portuguese Language. PROPOR 2014. Lecture Notes in Computer Science(), vol 8775. Springer, Cham. https://doi.org/10.1007/978-3-319-09761-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-09761-9_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09760-2

  • Online ISBN: 978-3-319-09761-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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