Title:
Towards robust conversational speech recognition and understanding
Towards robust conversational speech recognition and understanding
Author(s)
Weng, Chao
Advisor(s)
Lee, Chin-Hui
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Abstract
While significant progress has been made in automatic speech recognition (ASR) during the last few decades, recognizing and understanding unconstrained conversational speech remains a challenging problem. In this dissertation, five methods/systems are proposed towards a robust conversational speech recognition and understanding system.
I. A non-uniform minimum classification error (MCE) approach is proposed which can achieve consistent and significant keyword spotting performance gains on both English and Mandarin large-scale spontaneous conversational speech tasks (Switchboard and HKUST Mandarin CTS).
II. A hybrid recurrent DNN-HMM system is proposed for robust acoustic modeling and a new way of backpropagation through time (BPTT) is introduced. The proposed system achieves state-of-the-art performances on two benchmark datasets, the 2nd CHiME challenge (track 2) and Aurora-4, without front-end preprocessing, speaker adaptive training or multiple decoding passes.
III. To study the specific case of conversational speech recognition in the presence of competing talkers, several multi-style training setups of DNNs are investigated and a joint decoder operating on multi-talker speech is introduced. The proposed combined system improves upon the previous state-of-the-art IBM superhuman system by 2.8% absolute on the 2006 speech separation challenge dataset.
IV. Latent semantic rational kernels (LSRKs) are proposed for spotting the semantic notions on conversational speech. The proposed framework is generalized using tf-idf weighting, latent semantic analysis, WordNet, probabilistic topic models and neural network learned representations and is shown to achieve substantial topic spotting performance gains on two conversational speech tasks, Switchboard and AT&T HMIHY initial collection.
V. Non-uniform sequential discriminative training (DT) of DNNs with LSRKs is proposed which directly links the information of the proposed LSRK framework to the objective function of the DT. The experimental results on the subset of Switchboard show the proposed method can lead the acoustic modeling to a more robust system with respect to the semantic decoder.
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Date Issued
2014-08-19
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Dissertation