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From Sphinx-II to Whisper — Making Speech Recognition Usable

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Automatic Speech and Speaker Recognition

Part of the book series: The Kluwer International Series in Engineering and Computer Science ((SECS,volume 355))

Abstract

In this chapter, we first review Sphinx-II, a large-vocabulary speaker-independent continuous speech recognition system developed at Carnegie Mellon University, summarizing the techniques that helped Sphinx-II achieve state-of-the-art recognition performance. We then review Whisper, a system we developed here at Microsoft Corporation, focusing on recognition accuracy, efficiency and usability issues. These three issues are critical to the success of commercial speech applications. Whisper has significantly improved its performance in these three areas. It can be configured as a spoken language front-end (telephony or desktop) or dictation application.

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References

  1. A. Acero, “Acoustical and Environmental Robustness in Automatic Speech Recognition”. Kluwer Academic Publishers. 1993.

    Book  Google Scholar 

  2. A.V. Aho and J.D. Ullman, “Principles of Computer Design”. Addison Wesley. 1985.

    Google Scholar 

  3. F. Alleva, X. Huang, and M. Hwang, “An Improved Search Algorithm for Continuous Speech Recognition”. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993.

    Google Scholar 

  4. F. Alleva, “Search Organization for Large Vocabulary Continuous Speech Recognition”, Speech Recognition and Understanding: Recent Advances, Trends, and Applications, Laface and De Mori ed. Springer-Verlag, Berlin, 1992.

    Google Scholar 

  5. Asadi, R. Schwartz, and J. Makhoul, “Automatic Modeling of Adding New Words to a Large-Vocabulary Continuous Speech Recognition System”. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1991.

    Google Scholar 

  6. L. Bahl, R. Bakis, F. Jelinek, and R. Mercer, “Language-Model/Acoustic Channel Balance Mechanism”. IBM Technical Disclosure Bulletin, vol. 23 (1980), pp. 3464–3465.

    Google Scholar 

  7. L. Bahl, F. Jelinek, and R. Mercer, “A Maximum Likelihood Approach to Continuous Speech Recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5 (1983), pp. 179–190.

    Article  Google Scholar 

  8. Breiman, Friedman, Olshen, and Stone. “Classification and Regression Trees”. Wadsworth, Inc., Belmont, CA., 1984.

    MATH  Google Scholar 

  9. S. Davis and F. Mermelstein, “Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences”. IEEE Trans, on Acoustics, Speech and Signal Processing. ASSP-28:375–366. Aug. 1980.

    Google Scholar 

  10. Duda and Hart, “Pattern Recognition and Scene Analysis”. Wiley. 1973

    Google Scholar 

  11. J. Gauvain and C. Lee, “Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains”. IEEE Trans, on Speech and Audio Processing, Vol. 2, pp. 291–298, 1994.

    Article  Google Scholar 

  12. H. Hon, “CMU Vocabulary-Independent Speech Recognition System”. Ph.D. Thesis, School of Computer Science, Carnegie Mellon University, 1992.

    Google Scholar 

  13. X. Huang, Y. Ariki and M. Jack, “Hidden Markov Models for Speech Recognition”. Edinburgh University Press, Edinburgh, 1990.

    Google Scholar 

  14. X. Huang, “Phoneme Classification Using Semicontinuous Hidden Markov Models”. IEEE Transactions on Signal Processing, vol. 40 (1992), pp. 1062–1067.

    Article  Google Scholar 

  15. X. Huang, “Minimizing Speaker Variations Effects for Speaker-Independent Speech Recognition”. Proceedings of DARPA Speech and Language Workshop, Morgan Kaufmann Publishers, San Mateo, CA, 1992.

    Google Scholar 

  16. X. Huang, F. Alleva, M. Hwang, and R. Rosenfeld, “An Overview of Sphinx-II Speech Recognition System”. Proceedings of ARPA Human Language Technology Workshop, March 1993.

    Google Scholar 

  17. X. Huang, M. Belin, F. Alleva, and M. Hwang, “Unified Stochastic Engine (USE) for Speech Recognition”. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993.

    Google Scholar 

  18. X. Huang and K. Lee, “On Speaker-Independent, Speaker-Dependent, and Speaker-Adaptive Speech Recognition”. IEEE Transaction on Speech and Audio Processing, Vol. 1, pp. 150–157, 1993.

    Article  Google Scholar 

  19. M. Hwang, H. Hon, and K. Lee, “Modeling Inter-Word Coarticulation Using Generalized Triphones”. The 117th Meeting of the Acoustical Society of America, May 1989.

    Google Scholar 

  20. M. Hwang, H. Hon, and K. Lee, “Modeling Between-Word Coarticulation in Continuous Speech Recognition”. Proceedings of Eurospeech, pp. 5–8, September, 1989.

    Google Scholar 

  21. M. Hwang, and X. Huang, “Subphonetic Modeling with Markov States-Senone”. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992.

    Google Scholar 

  22. M. Hwang and X. Huang, “Shared-Distribution Hidden Markov Models for Speech Recognition”. IEEE Transactions on Speech and Audio Processing, vol. 1 (1993).

    Google Scholar 

  23. M. Hwang, X. Huang, and F. Alleva, “Predicting Unseen Triphones with Senones”. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993.

    Google Scholar 

  24. B. Juang, and Katagiri, “Discriminative Learning for Minimum Error Classification”. IEEE Trans, on Signal Processing, to appear.

    Google Scholar 

  25. K. Lee, H. Hon, and R. Reddy, “An Overview of the Sphinx Speech Recognition System”. IEEE Transactions on Acoustics, Speech, and Signal Processing, January 1990, pp. 35–45.

    Google Scholar 

  26. K. Lee, “Context-Dependent Phonetic Hidden Markov Models for Continuous Speech Recognition”. IEEE Transactions on Acoustics, Speech, and Signal Processing, April 1990, pp. 599–609.

    Google Scholar 

  27. K. Lee, “The Conversational Computer: An Apple Perspective”. Proceedings of the Eurospeech Conference. Berlin, Germany. Sep. 1993.

    Google Scholar 

  28. F. Liu, R. Stern, X. Huang, and A. Acero, “Efficient Cepstral Normalization for Robust Speech Recognition”. Proceedings of ARPA Human Language Technology Workshop, March 1993.

    Google Scholar 

  29. B. Lowerre and R. Reddy, “The Harpy Speech Understanding System”. Prentice-Hall, Englewood Cliffs, NJ, 1980.

    Google Scholar 

  30. Microsoft Corporation. “Speech API Specification”. Microsoft Press. 1995.

    Google Scholar 

  31. H. Ney, R. Haeb-Umbach, B. Tran, and M. Oerder, “Improvements in Beam Search for 10000 Word Continuous Speech Recognition”. IEEE International Conference on Acoustics, Speech, and Signal Processing, March 1992.

    Google Scholar 

  32. H. Ney, “Modeling and Search in Continuous Speech Recognition”, ESCA 3rd European Conference on Speech Communication and Technology, September 1993

    Google Scholar 

  33. Pallett, Fiscus, Fisher, and Garofolo. “Benchmark Tests for the DARPA Spoken Language Program”. Proceedings of ARPA Human Language Technology Workshop, March 1993.

    Google Scholar 

  34. R. Schwartz, S. Austin, F. Kubala, and J. Makhoul, “New Uses for the N-Best Sentence Hypotheses Within the Byblos Speech Recognition System”. IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992.

    Google Scholar 

  35. K. Shikano, “Evaluation of LPC Spectral Matching Measures for Phonetic Unit Recognition”. Technical Report, Computer Science Department, Carnegie Mellon University. May 1986.

    Google Scholar 

  36. F. Soong and E. Huang, “A Tree-Trellis Based Fast Search”. Proceedings of DARPA Speech and Language Workshop. 1990.

    Google Scholar 

  37. Viterbi, “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm”. IEEE Transactions on Information Theory, vol. IT-D (1967), pp. 260–269.

    Article  Google Scholar 

  38. W. Ward, “Modeling Non-Verbal Sounds for Speech Recognition”. Proceedings of DARPA Speech and Language Workshop, October 1989.

    Google Scholar 

  39. J. Wilpon, L. Rabiner, C. Lee, and Goldman, “Automatic Recognition of Keywords in Unconstrained Speech using Hidden Markov Models”. IEEE Trans, on Acoustics, Speech, and Signal Processing, Vol.- ASSP-38, pp. 1870–1878, 1990.

    Google Scholar 

  40. V. Zue, J. Glass, Goodine, Leung, McCandless, Philips, Polifroni, and Seneff, “Recent Progress on the Voyager System”. Proceedings of DARPA Speech and Language Workshop, 1990.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Microsoft Corporation, Redmond, WA, 98052, USA

    X. Huang, A. Acero, F. Alleva, M. Hwang, L. Jiang & M. Mahajan

Authors
  1. X. Huang
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  2. A. Acero
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  3. F. Alleva
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  4. M. Hwang
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  5. L. Jiang
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  6. M. Mahajan
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Editor information

Editors and Affiliations

  1. AT&T Bell Laboratories, Murray Hill, NJ, 07974, USA

    Chin-Hui Lee  & Frank K. Soong  & 

  2. School of Microelectronic Engineering, Griffith University, Australia

    Kuldip K. Paliwal

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© 1996 Kluwer Academic Publishers

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Huang, X., Acero, A., Alleva, F., Hwang, M., Jiang, L., Mahajan, M. (1996). From Sphinx-II to Whisper — Making Speech Recognition Usable. In: Lee, CH., Soong, F.K., Paliwal, K.K. (eds) Automatic Speech and Speaker Recognition. The Kluwer International Series in Engineering and Computer Science, vol 355. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1367-0_20

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  • DOI: https://doi.org/10.1007/978-1-4613-1367-0_20

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8590-8

  • Online ISBN: 978-1-4613-1367-0

  • eBook Packages: Springer Book Archive

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