Abstract
In this work, we deal with advanced context-dependent automatic speech recognition (ASR) of Czech spontaneous talk using hidden Markov models (HMM). Context-dependent units (e.g. triphones, diphones) in ASR systems provide significant improvement against simple non-context-dependent units. However, for spontaneous speech recognition we had to overcome some very challenging tasks. For one, the number of syllables compared to the size of spontaneous speech corpus makes the usage of context-dependent units very difficult. The main part of this article shows problems and procedures to effectively build and use a syllable-based ASR with the LASER (ASR system developed at Department of Computer Science and Engineering, Faculty of Applied Sciences). The procedures are usable with virtual any modern ASR.
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References
Lánský, J., Žemlička, M.: Text Compression: Syllables. In: Proceedings of the Dateso 2005 Annual International Workshop on Databases, Texts, Specifications and Objects. CEUR-WS, vol. 129, pp. 32–45 (2005)
Hejtmánek, J.: Use of Context-Dependent Units in Speech Recognition. Master thesis, University of West Bohemia in Pilsen, Faculty of Applied Sciences (2007)
Hejtmánek, J., Pavelka, T.: Use of Context-Dependent Units in Czech Speech. In: Proc. of Ph.D. Workshop 2007, Balatonfred, Hungary (2007)
Young, S., Evermann, G., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book (for HTK Version 3.3), Cambridge University Engineering Department (2005)
Yu, K., Mason, J., Oglesby, J.: Speaker Recognition Models. In: Proceedings of Eurospeech 1995, pp. 629–632 (1995)
Laurinčukaité, S., Lipeika, A.: Syllable-Phoneme Based Continuous Speech Recognition. Institute of Mathematics and Informatics, Vilnius (2006)
Chang, S.: A Syllable, Articulatory-Feature and Stress-Accent Model of Speech Recognition. Berkeley. Ph.D. thesis. International Computer Science Institute (2002)
Ananthakrishnan, S., Narayanan, S.: Improved Speech Recognition Using Acoustic and Lexical Correlates of Pitch Accent in a N-best Rescoring Framework. Speech Analysis and Interpretation Laboratory Department of Electrical Engineerig Viterbi School of Engineering University of Southern California, Los Angeles (2007)
Chen, K., Hasegawa-Johnson, M., Cohen, A.: An automatic Prosody Labeling System Using ANN-based Syntactic-Prosodic Model and GMM-Based Acoustic-Prosodic Model. In: International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 509–512 (2004)
Han, Y., Boves, L.: EM Algorithm with Split and Merge in Trajectory Clustering for Automatic Speech Recognition. Department of Language and Speech, Radboud University Nijmegen (2006)
Shafran, I., Ostendorf, M.: Acoustic Model Clustering Based on Syllable Structure. Washington, Department of Electrical Engineering (2002)
SIL International, Glosary of linguistic Terms (2008), http://www.sil.org
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Hejtmánek, J. (2010). Using Syllables as Acoustic Units for Spontaneous Speech Recognition. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_38
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DOI: https://doi.org/10.1007/978-3-642-15760-8_38
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