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Advanced ASR Technologies for Mitsubishi Electric Speech Applications

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New Era for Robust Speech Recognition
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Abstract

Mitsubishi Electric Corporation has been developing speech applications for 20 years. Our main targets are car navigation systems, elevator-controlling systems, and other industrial devices. This chapter deals with automatic speech recognition technologies which were developed for these applications. To realize real-time processing with small resources, syllable N-gram-based text search is proposed. To deal with reverberant environments in elevators, spectral-subtraction-based dereverberation techniques with reverberation time estimation are used. In addition, discriminative methods for acoustic and language models are developed.

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References

  1. Boll, S.: Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust. Speech Signal Process. 27(2), 113–120 (1979)

    Article  Google Scholar 

  2. Diehl, F., Woodland, P.: Complementary phone error training. In: Proceedings of INTERSPEECH (2012)

    Google Scholar 

  3. Fiscus, J.: A post-processing system to yield reduced error word rates: recognizer output voting error reduction (ROVER). In: Proceedings of ASRU, pp. 347–354 (1997)

    Google Scholar 

  4. Hanazawa, T., Okato, Y., Iwasaki, T.: Speech recognition using statistical language model and text match based large vocabulary search by voice. In: Proceedings of 2009 Autumn Meeting of the Acoustical Society of Japan, pp. 61–62 (2009)

    Google Scholar 

  5. Iwasaki, T., Kosaka, M., Nanba, T., Narita, T.: Voice interface of car navigation system – current technologies and the future. In: Mitsubishi Denki Giho, pp. 51–54 (2004)

    Google Scholar 

  6. Lebart, K., Boucher, J.M., Denbigh, P.N.: A new method based on spectral subtraction for speech dereverberation. Acta Acustica 87, 359–366 (2001)

    Google Scholar 

  7. Mikolov, T., Karafiát, M., Burget, L., C̆ernocký, J., Khudanpur, S.: Recurrent neural network based language model. In: Proceedings of INTERSPEECH, pp. 1045–1048 (2010)

    Google Scholar 

  8. Nakayama, M., Nishiura, T., Denda, Y., Kitaoka, N., Yamamoto, K., Yamada, T., Tsuge, S., Miyajima, C., Fujimoto, M., Takiguchi, T., Tamura, S., Ogawa, T., Matsuda, S., Kuroiwa, S., Takeda, K., Nakamura, S.: CENSREC-4: development of evaluation framework for distant-talking speech recognition under reverberant environments. In: Proceedings of Interspeech, pp. 968–971 (2008)

    Google Scholar 

  9. Naylor, P., Gaubitch, N.: Speech Dereverberation. Springer, New York (2010)

    Book  MATH  Google Scholar 

  10. Tachioka, Y., Watanabe, S.: Discriminative training of acoustic models for system combination. In: Proceedings of INTERSPEECH, pp. 2355–2359 (2013)

    Google Scholar 

  11. Tachioka, Y., Watanabe, S., Hershey, J.: Effectiveness of discriminative training and feature transformation for reverberated and noisy speech. In: Proceedings of ICASSP, pp. 6935–6939 (2013)

    Google Scholar 

  12. Tachioka, Y., Watanabe, S., Le Roux, J., Hershey, J.: A generalized framework of discriminative training for system combination. In: Proceedings of ASRU, pp. 43–48 (2013)

    Google Scholar 

  13. Vincent, E., Barker, J., Watanabe, S., Le Roux, J., Nesta, F., Matassoni, M.: The second “CHiME” speech separation and recognition challenge: datasets, tasks and baselines. In: Proceedings of ICASSP, pp. 126–130 (2013)

    Google Scholar 

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Correspondence to Yuuki Tachioka .

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Tachioka, Y., Hanazawa, T., Narita, T., Ishii, J. (2017). Advanced ASR Technologies for Mitsubishi Electric Speech Applications. In: Watanabe, S., Delcroix, M., Metze, F., Hershey, J. (eds) New Era for Robust Speech Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-64680-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-64680-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64679-4

  • Online ISBN: 978-3-319-64680-0

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