Abstract
Discriminative language models (DLMs) have been widely used for reranking competing hypotheses produced by an Automatic Speech Recognition (ASR) system. While existing DLMs suffer from limited generalization power, we propose a novel DLM based on a discriminatively trained Restricted Boltzmann Machine (RBM). The hidden layer of the RBM improves generalization and allows for employing additional prior knowledge, including pre-trained parameters and entity-related prior. Our approach outperforms the single-layer-perceptron (SLP) reranking model, and fusing our approach with SLP achieves up to 1.3% absolute Word Error Rate (WER) reduction and a relative 180% improvement in terms of WER reduction over the SLP reranker. In particular, it shows that proposed prior informed RBM reranker achieves largest ASR error reduction (3.1% absolute WER) on content words.
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References
Collins, M.: Ranking algorithms for named-entity extraction: boosting and the voted perceptron. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 489–496 (2002)
Ma, Y., Cambria, E., Gao, S.: Label embedding for zero-shot fine-grained named entity typing. In: COLING, Osaka, pp. 171–180 (2016)
Collins, M., Koo, T.: Discriminative reranking for natural language parsing. Comput. Linguist. 31, 25–70 (2005)
Koo, T., Collins, M.: Hidden-variable models for discriminative reranking. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada, pp. 507–514 (2005)
Li, Z., Khudanpur, S.: Large-scale discriminative n-gram language models for statistical machine translation. In: Proceedings of AMTA (2009)
Roark, B., Saraclar, M., Collins, M., Johnson, M.: Discriminative language modeling with conditional random fields and the perceptron algorithm. In: Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL 2004), Main Volume, Barcelona, Spain, pp. 47–54 (2004)
Collins, M., Roark, B., Saraclar, M.: Discriminative syntactic language modeling for speech recognition. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 507–514 (2005)
Lambert, B., Raj, B., Singh, R.: Discriminatively trained dependency language modeling for conversational speech recognition. In: INTERSPEECH, pp. 3414–3418 (2013)
Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 194–281 (1986)
Niehues, J., Waibel, A.: Continuous space language models using restricted boltzmann machines. In: IWSLT, pp. 164–170 (2012)
Dahl, G.E., Adams, R.P., Larochelle, H.: Training restricted boltzmann machines on word observations. arXiv preprint arXiv:1202.5695 (2012)
Wang, L., Liu, K., Cao, Z., Zhao, J., de Melo, G.: Sentiment-aspect extraction based on restricted boltzmann machines. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1: Long Papers. Association for Computational Linguistics, Beijing (2015)
Fries, C.C.: The Structure of English. Harcourt Brace, New York (1952)
Pernkopf, F., Wohlmayr, M., Tschiatschek, S.: Maximum margin Bayesian network classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 34, 521–532 (2012)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771–1800 (2002)
Levit, M., Parthasarathy, S., Chang, S.: Word-phrase-entity language models: Getting more mileage out of n-grams. In: Proceedings of Interspeech, Singapore, ISCA - International Speech Communication Association, pp. 666–670 (2014)
Salakhutdinov, R., Hinton, G.E.: Replicated softmax: an undirected topic model. In: NIPS, vol. 22, pp. 1607–1614 (2009)
Rousseau, A., Deléglise, P., Estève, Y.: Enhancing the TED-LIUM corpus with selected data for language modeling and more ted talks. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 3935–3939 (2014)
Ferraresi, A., Zanchetta, E., Baroni, M., Bernardini, S.: Introducing and evaluating ukWaC, a very large web-derived corpus of English. In: Proceedings of WAC-4 (2008)
Povey, D., et al.: The kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. IEEE Signal Processing Society (2011)
Walker, W., et al.: Sphinx-4: a flexible open source framework for speech recognition. Technical report, Mountain View, CA, USA (2004)
Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363–370. Association for Computational Linguistics (2005)
Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (1998)
Cambria, E., Poria, S., Bajpai, R., Schuller, B.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING, pp. 2666–2677 (2016)
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This work was conducted within the Rolls-Royce@NTU Corp Lab with support from the National Research Foundation Singapore under the Corp Lab@University Scheme.
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Ma, Y., Cambria, E., Bigot, B. (2018). ASR Hypothesis Reranking Using Prior-Informed Restricted Boltzmann Machine. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_39
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DOI: https://doi.org/10.1007/978-3-319-77113-7_39
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