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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Other
Subjects: P1, PE, QA75
The work presented here suggests a method for assessing speech accommodation in a holistic acoustic manner by utilising Hidden Markov Models (HMMs). The rationale for implementation of this method is presented along with an explanation of how HMMs work. Here, a heavily simplified HMM is used (single state; mixture of gaussians) in order to assess the applicability of more sophisticated HMMs. Results are presented from a small-scale study of six pairs of female Scottish-English speakers, showing measurement of significant trends and changes in holistic acoustic features of speakers during conversational interaction. Our findings suggest that methods integrating HMMs with current holistic acoustic measures of speech may be a useful tool in accounting for acoustic change due to speaker interaction.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Babel, M. 2009. Selective vowel imitation in spontaneous phonetic accommodation. UC Berkeley Phonology Lab Annual Report (2009) 163-194.
    • [2] Babel, M. 2010. Dialect divergence and convergence in new zealand english. Language in Society 39(04), 437-456.
    • [3] Babel, M., Bulatov, D. 2012. The role of fundamental frequency in phonetic accommodation. Language and speech 55(2), 231-248.
    • [4] Babel, M., McAuliffe, M., McGuire, G. 2014. Spectral similarity and listener judgments of phonetic accommodation. Proceedings of the 10th International Seminar on Speech Production, Cologne, Germany.
    • [5] Baker, R., Hazan, V. 2011. Diapixuk: task materials for the elicitation of multiple spontaneous speech dialogs. Behavior research methods 43(3), 761-770.
    • [6] Boersma, P., Weenink, D. Dec. 2012. Praat: doing phonetics by computer [computer program].
    • [7] Bulatov, D. 2009. The effect of fundamental frequency on phonetic convergence. Berkeley Phonology Lab Annual Report 2009, 404-434.
    • [8] Casasanto, L. S., Jasmin, K., Casasanto, D. 2010. Virtually accommodating: Speech rate accommodation to a virtual interlocutor. Proceedings of the 32nd Annual Conference of the Cognitive Science Society 127-132.
    • [9] Finlayson, I., Lickley, R., Corley, M. 2012. Convergence of speech rate: Interactive alignment beyond representation'. Twenty-Fifth Annual CUNY Conference on Human Sentence Processing, CUNY Graduate School and University Center, New York, USA 24.
    • [10] Fromont, R., Hay, J. 2008. Onze miner: the development of a browser-based research tool. Corpora 3(2), 173-193.
    • [11] Ganchev, T., Fakotakis, N., Kokkinakis, G. 2005. Comparative evaluation of various mfcc implementations on the speaker verification task. Proceedings of the SPECOM volume 1 191-194.
    • [12] Giles, H., Coupland, J., Coupland, N. 1991. Contexts of accommodation: Developments in applied sociolinguistics. Cambridge University Press.
    • [13] Huckvale, M. 2007. ACCDIST: An accent similarity metric for accent recognition and diagnosis. In: Müller, C., (ed), Speaker Classification II Springer Lecture Notes in Computer Science. Springer Berlin Heidelberg.
    • [14] Kleiner, M., Brainard, D., Pelli, D., Ingling, A., Murray, R., Broussard, C. 2007. What's new in psychtoolbox-3. Perception 36(14), 1-1.
    • [15] Mistry, D. S., Kulkarni, A. 2013. Overview: Speech recognition technology, mel-frequency cepstral coefficients (mfcc), artificial neural network (ann). International Journal of Engineering Research and Technology volume 2. ESRSA Publications.
    • [16] Olivola, C. Y., Funk, F., Todorov, A. 2014. Social attributions from faces bias human choices. Trends in Cognitive Sciences 18(11), 566 - 570.
    • [17] Pardo, J. S. 2006. On phonetic convergence during conversational interaction. The Journal of the Acoustical Society of America 119, 2382.
    • [18] Pardo, J. S., Gibbons, R., Suppes, A., Krauss, R. M. 2011. Phonetic convergence in college roommates. Journal of Phonetics.
    • [19] Todorov, A., Olivola, C. Y., Dotsch, R., MendeSiedlecki, P. 2015. Social attributions from faces: Determinants, consequences, accuracy, and functional significance. Annual Review of Psychology 66(1), 519-545. PMID: 25196277.
    • [20] Young, S. J., Evermann, G., Gales, M. J. F., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P. C. 2006. The HTK Book, version 3.4. Cambridge, UK: Cambridge University Engineering Department.
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