Abstract
The ASR decoder is one of the fundamental components of an ASR system and has been evolving over the years to address the increasing demands for larger domains as well as the availability of more powerful hardware. Though the basic search algorithm (i.e. Viterbi search) is relatively simple, implementing a decoder which can handle hundreds of thousands of words in the active vocabulary and hundreds of millions of n-grams in the language model in real time is no simple task. With the emergence of embedded platforms, some of the design concepts used in the past to cope with limitations of the available hardware can become relevant again, where such limitations are similar to those of workstations of early days of ASR. In this paper we will describe various basic design concepts encountered in various decoder implementations, with the focus on those which are relevant today among the fairly large spectrum of available hardware platforms.
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References
Bahl, L.R., Jelinek, F., Mercer, R.L.: A maximum likelihood approach to continuous speech recognition. IEEE Transactions on Pattern Analysis and Machine Inteligence 5(2), 179–190 (1983)
Bahl, L.R., De Souza, P.V., Gopalakrishnan, P.S., Nahamoo, D., Picheny, M.A.: Context dependent modelling of phones in continuous speech using decision trees. In: Proceedings DARPA Speech and Natural Language Processing Workshop, pp. 264–270 (1991)
Gopalakrishnan, P.S., Bahl, L.R., Mercer, R.L.: A tree search strategy for large vocabulary continuous speech recognition. In: Proc. ICASSP 1995, May 1995, pp. 572–575 (1995)
Bahl, L.R., De Gennaro, S.V., Gopalakrishnan, P.S., Mercer, R.L.: A fast approximate acoustic match for large vocabulary speech recognition. IEEE Transactions on Speech and Audio Processing 1(1), 59–67 (1993)
Forney Jr., G.D.: The Viterbi algorithm. Proceedings of the IEEE 61, 268–278 (1973)
Katz, S.M.: Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoust., Speech and Signal Processing 35(3), 400–401 (1987)
Ortmanns, S., Eiden, A., Ney, H.: Improved lexical tree search for large vocabulary speech recognition. In: Proceedings of ICASSP 1998, vol. 2, pp. 817–820 (1998)
Mohri, M., Pereira, F., Riley, M.: Weighted finite-state transducers in speech recognition. Computer Speech & Language 16(1), 69–88 (2002)
Chen, A.: Compiling large-context phonetic decision trees into finite-state transducers, Geneva, Switzerland, pp. 1169–1172 (2003)
Novak, M.: Incremental composition of static decoding graphs. In: Proceedings of Eurospeech 2009, Brighton, UK (2009)
Caseiro, D., Trancose, I.: A specialized on-the-fly algorithm for lexicon and language model composition. IEEE Transactions on Audio, Speech and Language Processing 14(4), 1281–1291 (2006)
Schalkwyk, J., Hetherington, L., Story, E.: Speech recognition with dynamic grammars using finite-state transducers. In: Proc. of Eurospeech 2003, pp. 1969–1972 (2003)
Saon, G., Povey, D., Zweig, G.: Anatomy of an extremely fast lvcsr decoder. In: Proceedings of Interspeech 2005, pp. 549–552 (2005)
Novak, M.: Memory efficient approximative lattice generation for grammar based decoding. In: Proceedings of Eurospeech 2005, Lisbon, Portugal (2005)
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Novak, M. (2010). Evolution of the ASR Decoder Design. 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_3
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DOI: https://doi.org/10.1007/978-3-642-15760-8_3
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