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Speech Communication
Calculating Inverse Filters for Speech Dereverberation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Expansion of WFST-based dialog management for handling multiple ASR hypotheses
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
User-adaptive coordination of agent communicative behavior in spoken dialogue
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Large vocabulary speech recognition system: SPOJUS++
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
Index-based incremental language model for scalable directory assistance
Speech Communication
Joint estimation of confidence and error causes in speech recognition
Speech Communication
Computer Speech and Language
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This paper proposes a novel one-pass search algorithm with on-the-fly composition of weighted finite-state transducers (WFSTs) for large-vocabulary continuous-speech recognition. In the standard search method with on-the-fly composition, two or more WFSTs are composed during decoding, and a Viterbi search is performed based on the composed search space. With this new method, a Viterbi search is performed based on the first of the two WFSTs. The second WFST is only used to rescore the hypotheses generated during the search. Since this rescoring is very efficient, the total amount of computation required by the new method is almost the same as when using only the first WFST. In a 65k-word vocabulary spontaneous lecture speech transcription task, our proposed method significantly outperformed the standard search method. Furthermore, our method was faster than decoding with a single fully composed and optimized WFST, where our method used only 38% of the memory required for decoding with the single WFST. Finally, we have achieved high-accuracy one-pass real-time speech recognition with an extremely large vocabulary of 1.8 million words