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The AhoSR Automatic Speech Recognition System

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8854)

Abstract

AhoSR is a hidden Markov model based speech recognition system developed in the Aholab Signal Processing Laboratory research group of the University of the Basque Country. It has been modularly devised for ASR-based tools and applications to be easily implemented and tested, being also particularly interesting for research in the field of language model optimization of agglutinative languages like Basque. The system relies on the use of a static search graph where decoupled language model information is incorporated at run-time. This paper introduces the basic architecture as well as the most relevant aspects of the AhoSR speech recognition system. Besides, this paper compiles the results of several experiments which validate the system for its use in different tasks: phonetic, grammar-based and LM-based recognition. Two CALL/CAPT applications that use AhoSR are also described.

Keywords

  • speech recognition
  • Basque ASR
  • software

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References

  1. Young, S.J., Evermann, G., Gales, M.J.F., Hain, T., Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.C.: The HTK Book Version 3.4.1 (2009), http://htk.eng.cam.ac.uk/

  2. Lee, A., Kawahara, T.: Recent Development of Open-Source Speech Recognition Engine Julius. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Sapporo, Japan (2009)

    Google Scholar 

  3. Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., Silovsky, J., Stemmer, G., Vesely, K.: The Kaldi Speech Recognition Toolkit. In: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Waikoloa, USA (2011)

    Google Scholar 

  4. Rybach, D., Gollan, C., Heigold, G., Hoffmeister, B., Lf, J., Schlter, R., Ney, H.: The RWTH Aachen University Open Source Speech Recognition System. In: Conference of the International Speech Communication Association (Interspeech 2009), Brighton, United Kingdom, pp. 2111–2114 (2009)

    Google Scholar 

  5. Walker, W., Lamere, P., Kwok, P., Raj, B., Singh, R., Gouvea, E., Wolf, P., Woelfel, J.: Sphinx-4: A flexible open source framework for speech recognition. Technical report (2004)

    Google Scholar 

  6. Hirsimki, T., Creutz, M., Siivola, V., Kurimo, M., Virpioja, S., Pylkknen, J.: Unlimited vocabulary speech recognition with morph language models applied to Finnish. Computer Speech and Language 20(4), 515–541 (2006)

    CrossRef  Google Scholar 

  7. Sak, H., Saraclar, M., Gngr, T.: Morphology-based and sub-word language modeling for Turkish speech recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), Dallas, USA, pp. 14–19 (2010)

    Google Scholar 

  8. Choueiter, G., Povey, D., Chen, S.F., Zweig, G.: Morpheme-based language modeling for Arabic LVCSR. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), Toulouse, France, pp. 14–19 (2006)

    Google Scholar 

  9. Mihajlik, P., Fegy, T., Tske, Z., Ircing, P.: A morpho-graphemic approach for the recognition of spontaneous speech in agglutinative languages - like Hungarian. In: Conference of the International Speech Communication Association (Interspeech 2007), Antwerp, Belgium, pp. 27–31 (2007)

    Google Scholar 

  10. Thangarajan, R.: Speech Recognition for agglutinative languages. In: Modern Speech Recognition Approaches with Case Studies, ch. 2, pp. 37–56 (2012)

    Google Scholar 

  11. Guijarrubia, V.G., Torres, M.I., Justo, R.: Morpheme-based automatic speech recognition of basque. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. LNCS, vol. 5524, pp. 386–393. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  12. Luengo, I., Navas, E., Odriozola, I., Saratxaga, I., Hernaez, I., Sainz, I., Erro, D.: Modified LTSE-VAD Algorithm for Applications Requiring Reduced Silence Frame Misclassification. In: International Conference on Language Resources and Evaluation (LREC 2010), Valletta, Malta, pp. 1539–1544 (2010)

    Google Scholar 

  13. Viikki, O., Laurila, K.: Cepstral domain segmental feature vector normalization for noise robust speech recognition. Speech Communication 25(1-3), 133–147 (1998)

    CrossRef  Google Scholar 

  14. Rabiner, L.R.: A tutorial on HMM and selected applications in speech recognition. IEEE 77, 257–286 (1989)

    CrossRef  Google Scholar 

  15. Young, S., Odell, J., Woodland, P.: Tree-based state tying for high accuracy acoustic modelling. In: ARPA workshop on Human Language Technology (HLT), Plainsboro, USA, pp. 307–312 (1994)

    Google Scholar 

  16. Hunt, A., McGlashan, S.: Speech Recognition Grammar Specification. World Wide Web Consortium (2004), http://www.w3.org/TR/speech-grammar/

  17. Xiaolong, L., Yunxin, Z.: A fast and memory-efficient N-gram language model lookup method for large vocabulary continuous speech recognition. In: Computer Speech & Language, pp. 1–25 (2007)

    Google Scholar 

  18. Stolcke, A.: SRILM – an extensible language modeling toolkit. In: International Conference on Spoken Language Processing (ICSLP), Denver, USA, vol. 2, pp. 901–904 (2002)

    Google Scholar 

  19. Cardenal, A.: Realización de un reconocedor de voz en tiempo real para habla continua y grandes vocabularios. PhD. Thesis, Universidad de Vigo, Spain (2001) (in Spanish)

    Google Scholar 

  20. Demuynck, K., Duchateau, J., Compernolle, D., Wambacq, P.: An efficient search space representation for large vocabulary continuous speech recognition. Speech Communication 30(1), 37–53 (2000)

    CrossRef  Google Scholar 

  21. Young, S.J., Russell, N.H., Russell, J.H.S.: Token passing: A simple conceptual model for connected speech recognition systems. Cambridge University Engineering Dept. Tech. Rep. (1989)

    Google Scholar 

  22. Ortmanns, S., Ney, H.: Look-ahead techniques for fast beam search. Computer Speech and Language 14, 15–32 (2000)

    CrossRef  Google Scholar 

  23. Kanters, S., Cucchiarini, C., Strik, H.: The Goodness of Pronunciation algorithm: a detailed performance study. In: ISCA International Workshop on Speech and Language Technology in Education (SLaTE 2009), Warwickshire, United Kingdom, pp. 49–52 (2009)

    Google Scholar 

  24. Odriozola, I., Hernaez, I., Torres, M.I., Rodríguez-Fuentes, L.J., Penagarikano, M., Navas, E.: Basque Speecon-like and Basque SpeechDat MDB-600: speech databases for the development of ASR technology for Basque. In: International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland, pp. 2658–2665 (2014)

    Google Scholar 

  25. Johansen, F.T., Warakagoda, N., Lindberg, B., Lehtinen, G., Kacic, Z., Zgan, A., Elenius, K., Salvi, G.: COST 249 SpeechDat Multilingual Reference Recogniser. In: International Conference on Language Resources and Evaluation (LREC 2000), Athens, Greece, pp. 1351–1354 (2000)

    Google Scholar 

  26. Contemporary Reference Prose (Ereduzko Prosa Gaur) corpus, http://www.ehu.es/euskara-orria/euskara/ereduzkoa/ (in Basque)

  27. Ogawa, A., Takeda, K., Itakura, F.: Balancing acoustic and linguistic probabilities. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 1998), Seattle, USA, pp. 181–184 (1998)

    Google Scholar 

  28. Odriozola, I., Navas, E., Hernaez, I., Sainz, I., Saratxaga, I., Snchez, J., Erro, D.: Using an ASR database to design a pronunciation evaluation system in Basque. In: International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey, pp. 4122–4126 (2012)

    Google Scholar 

  29. Odriozola, I., Hernaez, I., Navas, E.: Design of a message verification tool to be implemented in CALL systems. In: Advances in Speech and Language Technologies for Iberian Languages (IberSPEECH 2012), Madrid, Spain, pp. 251–259 (2012)

    Google Scholar 

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Odriozola, I., Serrano, L., Hernaez, I., Navas, E. (2014). The AhoSR Automatic Speech Recognition System. In: , et al. Advances in Speech and Language Technologies for Iberian Languages. Lecture Notes in Computer Science(), vol 8854. Springer, Cham. https://doi.org/10.1007/978-3-319-13623-3_29

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  • DOI: https://doi.org/10.1007/978-3-319-13623-3_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13622-6

  • Online ISBN: 978-3-319-13623-3

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