Abstract
Language models (LMs) are an important field of study in automatic speech recognition (ASR) systems. LM helps acoustic models find the corresponding word sequence of a given speech signal. Without it, ASR systems would not understand the language and it would be hard to find the correct word sequence. During the past few years, researchers have tried to incorporate long-range dependencies into statistical word-based n-gram LMs. One of these long-range dependencies is topic. Unlike words, topic is unobservable. Thus, it is required to find the meanings behind the words to get into the topic. This research is based on the belief that nouns contain topic information. We propose a new approach for a topic-dependent LM, where the topic is decided in an unsupervised manner. Latent Semantic Analysis (LSA) is employed to reveal hidden (latent) relations among nouns in the context words. To decide the topic of an event, a fixed size word history sequence (window) is observed, and voting is then carried out based on noun class occurrences weighted by a confidence measure. Experiments were conducted on an English corpus and a Japanese corpus: The Wall Street Journal corpus and Mainichi Shimbun (Japanese newspaper) corpus. The results show that our proposed method gives better perplexity than the comparative baselines, including a word-based/class-based n-gram LM, their interpolated LM, a cache-based LM, a topic-dependent LM based on n-gram, and a topic-dependent LM based on Latent Dirichlet Allocation (LDA). The n-best list rescoring was conducted to validate its application in ASR systems.
- Bellegarda, J. R. 1998. A multi-span language modelling framework for large vocabulary speech recognition. IEEE Trans. Speech Audio Proc. 6, 456--457.Google ScholarCross Ref
- Bellegarda, J. R., Butzberger, J. W., Chow, Y.-L., Coccaro, N. B., and Naik, D. 1996. A novel word clustering algorithm based on latent semantic analysis. In Proceedings of the Acoustics, Speech, and Signal Processing (ASSP’96). 172--175. Google ScholarDigital Library
- Bilmes, J. A. and Kirchhoff, K. 2003. Factored language models and generalized parallel backoff. In Proceedings of the Human Language Technology Conference/North American Chapter of the Association for Computational Linguistics (HLT/NACCL’03). 4--6. Google ScholarDigital Library
- Blei, D. M., Ng, A. Y., Jordan, M. I., and Lafferty, J. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022. Google ScholarCross Ref
- Broman, S. and Kurimo, M. 2005. Methods for combining language models in speech recognition. In Proceedings of the European Conference on Speech Communication and Technology (INTERSPEECH’05). 1317--1320.Google Scholar
- Brown, P. F., Pietra, V. J. D., deSouza, P. V., Lai, J. C., and Mercer, R. L. 1990. Class-based n-gram models of natural language. Comput. Linguist. 18, 18--4. Google ScholarDigital Library
- Chen, B. 2009. Word topic models for spoken document retrieval and transcription. Trans. Asian Lang. Inform. Process. 8, 1, 1--27. Google ScholarDigital Library
- Chen, K. 1995. Topic identification in discourse. In Proceedings of the 7th Conference on European Chapter of the Association for Computational Linguistics (ACL’95). 267--271. Google ScholarDigital Library
- Dhillon, I. S., Fan, J., and Guan, Y. 2001. Efficient clustering of very large document collections. Data Mining for Scientific and Engineering Applications, V. K. R. Grossman, C. Kamath, and R. Namburu, eds. Kluwer Academic Publishers, 357--381.Google Scholar
- Gildea, D. and Hofmann, T. 1999. Topic-based language models using em. In Proceedings of the International Conference on Speech Communication and Technology (EUROSPEECH’99). 2167--2170.Google Scholar
- Hofmann, T. 1999. Probabilistic latent semantic analysis. In Proceedings of the Uncertainty in Artificial Intelligence (UAI’99). 289--296. Google ScholarDigital Library
- Iyer, R. and Ostendorf, M. 1996. Modeling long distance dependence in language: Topic mixtures vs. dynamic cache models. IEEE Trans. Speech Audio Proc. 236--239.Google Scholar
- Iyer, R., Ostendorf, M., and Rohlicek, J. R. 1994. Language modeling with sentence-level mixtures. In Proceedings of the Workshop on Human Language Technology (HLT’94). 82--87. Google ScholarDigital Library
- Jelinek, F. and Mercer, R. L. 1980. Interpolated estimation of markov source parameters from sparse data. In Proceedings of the Workshop on Pattern Recognition in Practice (WPPP’80).Google Scholar
- Kakkonen, T., Myller, N., Sutinen, E., and Timonen, J. 2008. Comparison of dimension reduction methods for automated essay grading. Educ. Tech. Soc. 11, 3, 275--288.Google Scholar
- Klakow, D. and Peters, J. 2002. Testing the correlation of word error rate and perplexity. Speech Comm. 38, 1-2, 19--28. Google ScholarDigital Library
- Kneser, R. and Ney, H. 1993. Improved clustering techniques for class-based statistical language modelling. In Proceedings of the European Conference on Speech Communication and Technology (EUROSPEECH’93). 973--976.Google Scholar
- Kneser, R. and Steinbiss, V. 1993. On the dynamic adaptation of stochastic lm. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’93). 2, 586--589.Google Scholar
- Kuhn, R. and de Mori, R. 1992. A cache based natural language model for speech recognition. IEEE Trans. Pattern Anal. Mach. Intell. 14, 570--583. Google ScholarDigital Library
- Liu, F. and Liu, Y. 2007. Unsupervised language model adaptation incorporating named entity information. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL’07). 672--679.Google Scholar
- Liu, Y. and Liu, F. 2008. Unsupervised language model adaptation via topic modeling based on named entity hypotheses. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’08). 4921--4924.Google Scholar
- Nakagawa, S. and Murase, I. 1992. Relationship among phoneme/word recognition rate, perplexity and sentence recognition and comparison of language models. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’92). 1, 589--592.Google Scholar
- Naptali, W., Masatoshi, T., and Nakagawa, S. 2009. Word co-occurrence matrix and context dependent class in lsa based language model for speech recognition. North Atlantic Univ. Union Inter. J. Comput. 3, 1, 85--95.Google Scholar
- Rosenfeld, R. 1996. A maximum entropy approach to additive statistical language modeling. Comput. Speech Lang.Google Scholar
- Schmid, H. 1994. Probabilistic part-of-speech tagging using decision trees. In Proceedings of the International Conference on New Methods in Language Processing (ICNMLP’94).Google Scholar
- Stolcke, A. 2002. Srilm -- An extensible language modeling toolkit. In Proceedings of the International Conference on Spoken Language Processing (ICSLP’02). 2, 901--904.Google Scholar
- Strik, H., Cucchiarini, C., and Kessens, J. M. 2000. Comparing the recognition performance of csrs: In search of an adequate metric and statistical significance test. In Proceedings of the International Conference on Spoken Language Processing (ICSLP’00). 740--744.Google Scholar
- Strik, H., Cucchiarini, C., and Kessens, J. M. 2001. Comparing the performance of two csrs: How to determine the significance level of the differences. In Proceedings of the European Conference on Speech Communication and Technology (EUROSPEECH’01). 3, 2091--2094.Google Scholar
- Yung, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., and Woodland, P. 2005. The HTK Book (for HTK version 3.3). Cambridge.Google Scholar
- Zhang, J., Wang, L., and Nakagawa, S. 2008. Lvcsr based on context dependent syllable acoustic models. In Proceedings of the Asian Workshop on Speech Science and Technology (SP’08). 81--86.Google Scholar
Index Terms
- Topic-Dependent Language Model with Voting on Noun History
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