Browsing by Author "Liao, YF"

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A modular RNN-based method for continuous Mandarin speech recognition
(2001-03-01)
Liao, YF
;
Chen, SH
;
電信工程研究所
;
Institute of Communications Engineering
A new modular recurrent neural network (MRNN)-based method for continuous Mandarin speech recognition (CMSR) is proposed. The MRNN recognizer is composed of four main modules. The first is a sub-MRNN module whose function is to generate discriminant functions for all 412 base-syllables. It accomplishes the task by using four recurrent neural network (RNN) submodules. The second is an RNN module which is designed to detect syllable boundaries for providing timing cues in order to help solve the time-alignment problem. The third is also an RNN module whose function is to generate discriminant functions for 143 intersyllable diphone-like units to compensate the intersyllable coarticulation effect. The fourth is a dynamic programming (DP)-based recognition search module. Its function is to integrate the other three modules and solve the time-alignment problem for generating the recognized base-syllable sequence. A new multilevel pruning scheme designed to speed up the recognition process is also proposed. The whole MRNN can be trained by a sophisticated three-stage minimum classification error/generalized probabilistic descent (MCE/GPD) algorithm. Experimental results showed that the proposed method performed better than the maximum likelihood (ML)-trained hidden Markov model (HMM) method and is comparable to the MCE/GPD-trained HMM method. The multilevel pruning scheme was also found to be very efficient.
An MRNN-based method for continuous Mandarin speech recognition
(1998)
Liao, YF
;
Chen, SH
;
電信工程研究所
;
Institute of Communications Engineering
A new MRNN-based method for continuous Mandarin speech recognition is proposed. The system uses five RNNs to accomplish many subtasks separately and then combine them to integrally solve the problem. They include two RNNs for the discriminations of the two sub-syllable groups of 100 RFD initials and 39 CI finals, two RNNs for the generations of dynamic weighting functions for sub-syllable's integration, and one RNN for syllable boundary detection. All RNN modules are combined using a delay-decision Viterbi search. The method differs from the ANN/HMM hybrid approach on using ANNs to perform not only sub-syllables discrimination but also temporal structure modeling of speech signal. The system is trained using a three-stage training method embedding with the MCE/GPD algorithms. Besides, fast recognition method using multi-level pruning is also proposed. Experimental results showed that it outperforms the HMM method on both the recognition accuracy and the computational complexity.
An RNN-based preclassification method for fast continuous Mandarin speech recognition
(1998-01-01)
Chen, SH
;
Liao, YF
;
Chiang, SM
;
Chang, SG
;
電信工程研究所
A novel recurrent neural network-based (RNN-based) front-end preclassification scheme for fast continuous Mandarin speech recognition is proposed in this paper, First, an RNN is employed to discriminate each input frame for the three broad classes of initial, final, and silence, A finite state machine (FSM) is then used to classify the input frame into four states including three stable states of Initial (I), Final (F), and Silence (S), and a Transient (T) state, The decision is made based on examining whether the RNN discriminates well between classes, We then restrict the search space for the three stable states in the following DP search to speed up the recognition process, Efficiency of the proposed scheme was examined by simulations in which we incorporate it with a hidden Markov model-based (HMM-based) continuous 411 Mandarin base-syllables recognizer, Experimental results showed that it can be used in conjunction with the beam search to greatly reduce the computational complexity of the HMM recognizer while keeping the recognition rate almost undegraded.
An RNN-based preclassification method for fast continuous Mandarin speech recognition
(1998-01-01)
Chen, SH
;
Liao, YF
;
Chiang, SM
;
Chang, SG
;
電信工程研究所
A novel recurrent neural network-based (RNN-based) front-end preclassification scheme for fast continuous Mandarin speech recognition is proposed in this paper, First, an RNN is employed to discriminate each input frame for the three broad classes of initial, final, and silence, A finite state machine (FSM) is then used to classify the input frame into four states including three stable states of Initial (I), Final (F), and Silence (S), and a Transient (T) state, The decision is made based on examining whether the RNN discriminates well between classes, We then restrict the search space for the three stable states in the following DP search to speed up the recognition process, Efficiency of the proposed scheme was examined by simulations in which we incorporate it with a hidden Markov model-based (HMM-based) continuous 411 Mandarin base-syllables recognizer, Experimental results showed that it can be used in conjunction with the beam search to greatly reduce the computational complexity of the HMM recognizer while keeping the recognition rate almost undegraded.
Continuous mandarin speech recognition using hierarchical recurrent neural networks
(1996)
Liao, YF
;
Chen, WY
;
Chen, SH
;
交大名義發表
;
National Chiao Tung University
Modular recurrent neural networks for Mandarin syllable recognition
(1998-11-01)
Chen, SH
;
Liao, YF
;
電信工程研究所
;
Institute of Communications Engineering
A new modular recurrent neural network (MRNN)-based speech-recognition method that can recognize the entire vocabulary of 1280 highly confusable Mandarin syllables is proposed in this paper, The basic idea is to first split the complicated task, in both feature and temporal domains, into several much simpler subtasks involving subsyllable and tone discrimination, and then to use two weighting RNN's to generate several dynamic weighting functions to integrate the subsolutions into a complete solution. The novelty of the proposed method lies mainly in the use of appropriate a priori linguistic knowledge of simple initial-final structures of Mandarin syllables in the architecture design of the MRNN, The resulting MRNN is therefore effective and efficient in discriminating among highly confusable Mandarin syllables. Thus both the time-alignment and scaling problems of the ANN-based approach for large-vocabulary speech-recognition can be addressed. Experimental results show that the proposed method and its extensions, the reverse-time MRNN (Rev-MRNN) and bidirection MRNN (Bi-MRNN), all outperform an advanced HMM method trained with the MCE/GPD algorithm in both recognition-rate and system complexity.
RNN-based prosodic modeling for mandarin speech and its application to speech-to-text conversion
(2002-03-01)
Wang, WJ
;
Liao, YF
;
Chen, SH
;
電信工程研究所
;
Institute of Communications Engineering
In this paper, a recurrent neural network (RNN) based prosodic modeling method for Mandarin speech-to-text conversion is proposed. The prosodic modeling is performed in the post-processing stage of acoustic decoding and aims at detecting word-boundary cues to assist in linguistic decoding. It employs a simple three-layer RNN to learn the relationship between input prosodic features, extracted from the input utterance with syllable boundaries pre-determined by the preceding acoustic decoder, and output word-boundary information of the associated text. After the RNN prosodic model is properly trained, it can be used to generate word-boundary cues to help the linguistic decoder solving the problem of word-boundary ambiguity. Two schemes of using these word-boundary cues are proposed. Scheme I modifies the baseline scheme of the conventional linguistic decoding search by directly taking the RNN outputs as additional scores and adding them to all word-sequence hypotheses to assist in selecting the best recognized word sequence. Scheme 2 is an extended version of Scheme I by further using the RNN outputs to drive a finite state machine (FSM) for setting path constraints to restrict the linguistic decoding search. Character accuracy rates of 73.6%, 74.6% and 74.7% were obtained for the systems using the baseline scheme, Schemes I and 2, respectively. Besides, a gain of 17% reduction in the computational complexity of the linguistic decoding search was also obtained for Scheme 2. So the proposed prosodic modeling method is promising for Mandarin speech recognition. (C) 2002 Elsevier Science B.V. All rights reserved.