Hitoshi Yamamoto


2012

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Factored Language Model based on Recurrent Neural Network
Youzheng Wu | Xugang Lu | Hitoshi Yamamoto | Shigeki Matsuda | Chiori Hori | Hideki Kashioka
Proceedings of COLING 2012

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The NICT ASR system for IWSLT2012
Hitoshi Yamamoto | Youzheng Wu | Chien-Lin Huang | Xugang Lu | Paul R. Dixon | Shigeki Matsuda | Chiori Hori | Hideki Kashioka
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our automatic speech recognition (ASR) system for the IWSLT 2012 evaluation campaign. The target data of the campaign is selected from the TED talks, a collection of public speeches on a variety of topics spoken in English. Our ASR system is based on weighted finite-state transducers and exploits an combination of acoustic models for spontaneous speech, language models based on n-gram and factored recurrent neural network trained with effectively selected corpora, and unsupervised topic adaptation framework utilizing ASR results. Accordingly, the system achieved 10.6% and 12.0% word error rate for the tst2011 and tst2012 evaluation set, respectively.

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Factored recurrent neural network language model in TED lecture transcription
Youzheng Wu | Hitoshi Yamamoto | Xugang Lu | Shigeki Matsuda | Chiori Hori | Hideki Kashioka
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

In this study, we extend recurrent neural network-based language models (RNNLMs) by explicitly integrating morphological and syntactic factors (or features). Our proposed RNNLM is called a factored RNNLM that is expected to enhance RNNLMs. A number of experiments are carried out on top of state-of-the-art LVCSR system that show the factored RNNLM improves the performance measured by perplexity and word error rate. In the IWSLT TED test data sets, absolute word error rate reductions over RNNLM and n-gram LM are 0.4∼0.8 points.