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Speaker Diarization: A Top-Down Approach Using Syllabic Phonology

  • Erik EdwardsEmail author
  • Amanda Robinson
  • Najmeh Sadoughi
  • Greg P. Finley
  • Maxim Korenevsky
  • Michael Brenndoerfer
  • Nico Axtmann
  • Mark Miller
  • David Suendermann-Oeft
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)

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.

Keywords

Speaker diarization Speech activity detection Syllable 

References

  1. 1.
    Anguera Miró, X.: Robust speaker diarization for meetings. Ph.D. thesis, Univ. Politècnica de Catalunya (2006)Google Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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_34CrossRefGoogle Scholar
  4. 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. 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. 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)CrossRefGoogle Scholar
  7. 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_51CrossRefGoogle Scholar
  8. 8.
    Fakotakis, N., Tsopanoglou, A., Kokkinakis, G.: A text-independent speaker recognition system based on vowel spotting. Speech Commun. 12(1), 57–68 (1993)CrossRefGoogle Scholar
  9. 9.
    Finley, G., et al.: An automated medical scribe for documenting clinical encounters. In: Proceedings of NAACL. ACL (2018)Google Scholar
  10. 10.
    Fudge, E.: Branching structure within the syllable. J. Linguist. 23(2), 359–377 (1987)CrossRefGoogle Scholar
  11. 11.
    Fujimura, O.: Syllable as a unit of speech recognition. IEEE Trans. Acoust. 23(1), 82–87 (1975)CrossRefGoogle Scholar
  12. 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. 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. 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. 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. 16.
    Guest, E.: A History of English Rhythms. W. Pickering, London (1838)Google Scholar
  17. 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. 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. 19.
    Kessler, B., Treiman, R.: Syllable structure and the distribution of phonemes in English syllables. J. Mem. Lang. 37(3), 295–311 (1997)CrossRefGoogle Scholar
  20. 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. 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)MathSciNetCrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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. 24.
    Mary, L., Yegnanarayana, B.: Extraction and representation of prosodic features for language and speaker recognition. Speech Commun. 50(10), 782–796 (2008)CrossRefGoogle Scholar
  25. 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. 26.
    Olson, H., Belar, H.: Phonetic typewriter. J. Acoust. Soc. Am. 28(6), 1072–1081 (1956)CrossRefGoogle Scholar
  27. 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. 28.
    Rudnicky, A.: CMUdict 0.7b: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA (2015). https://github.com/Alexir/CMUdict
  29. 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)CrossRefGoogle Scholar
  30. 30.
    Saussure, F.: Cours de linguistique générale. Payot, Lausanne, Paris (1916)Google Scholar
  31. 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. 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. 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. 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)CrossRefGoogle Scholar
  35. 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. 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. 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. 38.
    Wallis, J.: Grammatica linguae Anglicanae. L. Lichfield, Oxford (1674)Google Scholar
  39. 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. 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. 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. 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. 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

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Erik Edwards
    • 1
    Email author
  • Amanda Robinson
    • 1
  • Najmeh Sadoughi
    • 1
  • Greg P. Finley
    • 1
  • Maxim Korenevsky
    • 1
  • Michael Brenndoerfer
    • 2
  • Nico Axtmann
    • 3
  • Mark Miller
    • 1
  • David Suendermann-Oeft
    • 1
  1. 1.EMR.AI Inc.San FranciscoUSA
  2. 2.University of California BerkeleyBerkeleyUSA
  3. 3.DHBWKarlsruheGermany

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