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Speaker Diarization: A Top-Down Approach Using Syllabic Phonology

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Speech and Computer (SPECOM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11096))

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Abstract

A top-down approach to speaker diarization is developed using a modified Baum-Welch algorithm. The HMM states combine phonemes according to structural positions under syllabic phonological theory. By nature of the structural phonology, there are at most 16 states, and the transition matrix is sparse, allowing efficient decoding to structural phones. This addresses the issue of phoneme specificity in speaker diarization – that speaker similarities/differences are confounded by phonetic similarities/differences. We address this here without the expensive use of a complete set of individual phonemes. The voice activity detection (VAD) issue is likewise addressed, giving a new approach to VAD.

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References

  1. Anguera Miró, X.: Robust speaker diarization for meetings. Ph.D. thesis, Univ. Politècnica de Catalunya (2006)

    Google Scholar 

  2. Anguera Miró, X., Bozonnet, S., Evans, N., Fredouille, C., Friedland, G., Vinyals, O.: Speaker diarization: a review of recent research. IEEE Trans. Audio Speech Lang. Process. 20(2), 356–370 (2012)

    Article  Google Scholar 

  3. Anguera, X., Wooters, C., Peskin, B., Aguiló, M.: Robust speaker segmentation for meetings: the ICSI-SRI spring 2005 diarization system. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 402–414. Springer, Heidelberg (2006). https://doi.org/10.1007/11677482_34

    Chapter  Google Scholar 

  4. Bozonnet, S., Vipperla, R., Evans, N.: Phone adaptive training for speaker diarization. In: Proceedings of INTERSPEECH, pp. 494–497. ISCA (2012)

    Google Scholar 

  5. Chen, I.F., Cheng, S.S., Wang, H.M.: Phonetic subspace mixture model for speaker diarization. In: Proceedings of INTERSPEECH, pp. 2298–2301. ISCA (2010)

    Google Scholar 

  6. Cooper, F., Delattre, P., Liberman, A., Borst, J., Gerstman, L.: Some experiments on the perception of synthetic speech sounds. J. Acoust. Soc. Am. 24(6), 597–606 (1952)

    Article  Google Scholar 

  7. Edwards, E., et al.: Medical speech recognition: reaching parity with humans. In: Karpov, A., Potapova, R., Mporas, I. (eds.) SPECOM 2017. LNCS (LNAI), vol. 10458, pp. 512–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66429-3_51

    Chapter  Google Scholar 

  8. Fakotakis, N., Tsopanoglou, A., Kokkinakis, G.: A text-independent speaker recognition system based on vowel spotting. Speech Commun. 12(1), 57–68 (1993)

    Article  Google Scholar 

  9. Finley, G., et al.: An automated medical scribe for documenting clinical encounters. In: Proceedings of NAACL. ACL (2018)

    Google Scholar 

  10. Fudge, E.: Branching structure within the syllable. J. Linguist. 23(2), 359–377 (1987)

    Article  Google Scholar 

  11. Fujimura, O.: Syllable as a unit of speech recognition. IEEE Trans. Acoust. 23(1), 82–87 (1975)

    Article  Google Scholar 

  12. Gauvain, J.L., Adda, G., Lamel, L., Adda-Decker, M.: Transcribing broadcast news: the LIMSI Nov96 Hub4 system. In: Proceedings of DARPA Speech Recognition Workshop, pp. 56–63. DARPA (1997)

    Google Scholar 

  13. Ghahremani, P., BabaAli, B., Povey, D., Riedhammer, K., Trmal, J., Khudanpur, S.: A pitch extraction algorithm tuned for automatic speech recognition. In: Proceedings of ICASSP, pp. 2494–2498. IEEE (2014)

    Google Scholar 

  14. Gish, H., Siu, M.H., Rohlicek, J.: Segregation of speakers for speech recognition and speaker identification. In: Proceedings of ICASSP, vol. 2, pp. 873–876. IEEE (1991)

    Google Scholar 

  15. Goldsmith, J.: The syllable. In: Goldsmith, J., Riggle, J., Yu, A. (eds.) The Handbook of Phonological Theory, 2nd edn., pp. 165–196. Wiley, Malden (2011)

    Google Scholar 

  16. Guest, E.: A History of English Rhythms. W. Pickering, London (1838)

    Google Scholar 

  17. Hansen, E., Slyh, R., Anderson, T.: Speaker recognition using phoneme-specific GMMs. In: Proceedings of Odyssey Workshop, pp. 179–184. ISCA (2004)

    Google Scholar 

  18. Hsieh, C.H., Wu, C.H., Shen, H.P.: Adaptive decision tree-based phone cluster models for speaker clustering. In: Proceedings of INTERSPEECH, pp. 861–864. ISCA (2008)

    Google Scholar 

  19. Kessler, B., Treiman, R.: Syllable structure and the distribution of phonemes in English syllables. J. Mem. Lang. 37(3), 295–311 (1997)

    Article  Google Scholar 

  20. Kozhevnikov, V., Chistovich, L.: Speech: articulation and perception. Translation JPRS 30543, Joint Public Research Service, U.S. Department of Commerce (1965)

    Google Scholar 

  21. Levinson, S., Rabiner, L., Sondhi, M.: An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. Bell Syst. Tech. J. 62(4), 1035–1074 (1983)

    Article  MathSciNet  Google Scholar 

  22. Liberman, A., Ingemann, F., Lisker, L., Delattre, P., Cooper, F.: Minimal rules for synthesizing speech. J. Acoust. Soc. Am. 31(11), 1490–1499 (1959)

    Article  Google Scholar 

  23. Martin, T., Wong, E., Baker, B., Mason, M., Sridharan, S.: Pitch and energy trajectory modelling in a syllable length temporal framework for language identification. In: Proceedings of Odyssey Workshop, pp. 289–296. ISCA (2004)

    Google Scholar 

  24. Mary, L., Yegnanarayana, B.: Extraction and representation of prosodic features for language and speaker recognition. Speech Commun. 50(10), 782–796 (2008)

    Article  Google Scholar 

  25. Mitford, W.: An Inquiry into the Principles of Harmony in Language, and of the Mechanism of Verse, Modern and Antient, 2nd edn. L. Hansard, London (1804)

    Google Scholar 

  26. Olson, H., Belar, H.: Phonetic typewriter. J. Acoust. Soc. Am. 28(6), 1072–1081 (1956)

    Article  Google Scholar 

  27. Panayotov, V., Chen, G., Povey, D., Khudanpur, S.: LibriSpeech: an ASR corpus based on public domain audio books. In: Proceedings of ICASSP, pp. 5206–5210. IEEE (2015)

    Google Scholar 

  28. Rudnicky, A.: CMUdict 0.7b: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA (2015). https://github.com/Alexir/CMUdict

  29. Sadjadi, S., Hansen, J.: Unsupervised speech activity detection using voicing measures and perceptual spectral flux. IEEE Signal Process. Lett. 20(3), 197–200 (2013)

    Article  Google Scholar 

  30. Saussure, F.: Cours de linguistique générale. Payot, Lausanne, Paris (1916)

    Google Scholar 

  31. Schindler, C., Draxler, C.: Using spectral moments as a speaker specific feature in nasals and fricatives. In: Proceedings of INTERSPEECH, pp. 2793–2796. ISCA (2013)

    Google Scholar 

  32. Selkirk, E.: The syllable. In: van der Hulst, H., Smith, N. (eds.) The Structure of Phonological Representations, vol. 2, pp. 337–384. Foris, Dordrecht (1982)

    Google Scholar 

  33. Shoup, J.: Phonological aspects of speech recognition. In: Lea, W. (ed.) Trends in Speech Recognition, pp. 125–138. Prentice-Hall, Englewood Cliffs (1980)

    Google Scholar 

  34. Shriberg, E., Ferrer, L., Kajarekar, S., Venkataraman, A., Stolcke, A.: Modeling prosodic feature sequences for speaker recognition. Speech Commun. 46(3–4), 455–472 (2005)

    Article  Google Scholar 

  35. Siu, M.H., Yu, G., Gish, H.: An unsupervised, sequential learning algorithm for the segmentation of speech waveforms with multiple speakers. In: Proceedings of ICASSP, vol. 2, pp. 189–192. IEEE (1992)

    Google Scholar 

  36. Soldi, G., Bozonnet, S., Alegre, F., Beaugeant, C., Evans, N.: Short-duration speaker modelling with phone adaptive training. In: Proceedings of Odyssey Workshop, pp. 208–215. ISCA (2014)

    Google Scholar 

  37. Sugiyama, M., Murakami, J., Watanabe, H.: Speech segmentation and clustering based on speaker features. In: Proceedings of ICASSP, vol. 2, pp. 395–398. IEEE (1993)

    Google Scholar 

  38. Wallis, J.: Grammatica linguae Anglicanae. L. Lichfield, Oxford (1674)

    Google Scholar 

  39. Wang, G., Wu, X., Zheng, T.: Using phoneme recognition and text-dependent speaker verification to improve speaker segmentation for Chinese speech. In: Proceedings of INTERSPEECH, pp. 1457–1460. ISCA (2010)

    Google Scholar 

  40. Wilcox, L., Chen, F., Kimber, D., Balasubramanian, V.: Segmentation of speech using speaker identification. In: Proceedings of ICASSP, vol. 1, pp. 161–164. IEEE (1994)

    Google Scholar 

  41. Yamada, M., Pezeshki, A., Azimi-Sadjadi, M.: Relation between kernel CCA and kernel FDA. In: Proceedings of IJCNN, pp. 226–231. IEEE (2005)

    Google Scholar 

  42. Yella, S., Motlícek, P., Bourlard, H.: Phoneme background model for information bottleneck based speaker diarization. In: Proceedings of INTERSPEECH, pp. 597–601. ISCA (2014)

    Google Scholar 

  43. Zibert, J., Mihelic, F.: Prosodic and phonetic features for speaker clustering in speaker diarization systems. In: Proceedings of INTERSPEECH, pp. 1033–1036. ISCA (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. EMR.AI Inc., San Francisco, CA, USA

    Erik Edwards, Amanda Robinson, Najmeh Sadoughi, Greg P. Finley, Maxim Korenevsky, Mark Miller & David Suendermann-Oeft

  2. University of California Berkeley, Berkeley, CA, USA

    Michael Brenndoerfer

  3. DHBW, Karlsruhe, Germany

    Nico Axtmann

Authors
  1. Erik Edwards
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  2. Amanda Robinson
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  3. Najmeh Sadoughi
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  4. Greg P. Finley
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  5. Maxim Korenevsky
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  6. Michael Brenndoerfer
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  7. Nico Axtmann
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  8. Mark Miller
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  9. David Suendermann-Oeft
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Correspondence to Erik Edwards .

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Editors and Affiliations

  1. SPIIRAS, St. Petersburg, Russia

    Alexey Karpov

  2. Leipzig University of Telecommunications, Leipzig, Germany

    Oliver Jokisch

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Edwards, E. et al. (2018). Speaker Diarization: A Top-Down Approach Using Syllabic Phonology. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-99579-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99578-6

  • Online ISBN: 978-3-319-99579-3

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