Browsing by Author "Hong, WT"

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A prototype of Mandarin speech telephone number inquiry system
(1997)
Lu, PR
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Hong, WT
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Chiang, SL
;
Wang, YR
;
Chen, SH
In this paper, a prototype of Mandarin speech telephone number inquiry system is implemented on a PC platform with a Dialogic D/41D card and a 16-bit Soundblaster card as the telephone line interface. It is an integration of a speaker-independent, isolated-word speech recognition sub-system and a text-to-speech (TTS) subsystem, and is operating under Windows 95. The vocabulary to be recognized comprises 1922 names of bank and insurance companies with word lengths ranging from 2 to 14 syllables. A recognition rate of 92.6% was achieved. All output speech messages are responded by the TTS sub-system.
A robust training algorithm for adverse speech recognition
(2000-04-01)
Hong, WT
;
Chen, SH
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電信工程研究所
;
Institute of Communications Engineering
In this paper, anew robust training algorithm is proposed for the generation of a set of bias-removed, noise-suppressed reference speech HMM models in adverse environment suffering from both channel bias and additive noise. Its main idea is to incorporate a signal bias-compensation operation and a PMC noise-compensation operation into its iterative training process. This makes the resulting speech HMM models more suitable to the given robust speech recognition method using the same signal bias-compensation and PMC noise-compensation operations in the recognition process. Experimental results showed that the speech HMM models it generated outperformed both the clean-speech HMM models and those generated by the conventional k-means algorithm for two adverse Mandarin speech recognition tasks. So it is a promising robust training algorithm. (C) 2000 Elsevier Science B.V. All rights reserved.
A segment-based C-o adaptation scheme for PMC-based noisy Mandarin speech recognition
(1999)
Hong, WT
;
Chen, SH
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電信工程研究所
;
Institute of Communications Engineering
A segment-based C-0 (the zero-th order of cepstral coefficient) adaptation scheme for PMC-based Mandarin speech recognition is proposed in this paper. It incorporates a new C-0 model of speech signal into the PMC method to improve the gain matching between the clean-speech HMM models and the current noise model. The C-0 model is constructed in the training phase by jointly modeling the normalized C-0 with other MFCC recognition features to form C-0-normalized HMM models. In the testing phase, it pre-segments the input utterance into syllable-like segments, performs C-0-denormaliztion operations to expand the C-0-normalized HMM models, and uses them in the PMC method. Compared with the conventional PMC method, the proposed method can achieve a much better noise compensation effect due to the use of more precise gain matching in the PMC model combination. Experimental results showed that the base-syllable accuracy rate was significantly upgraded for continuous noisy Mandarin speech recognition.
An RNN-based noise estimation and likelihood compensation for noisy speech recognition
(1996)
Hong, WT
;
Chen, SH
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電信工程研究所
;
Institute of Communications Engineering
Robust SBR method for adverse Mandarin speech recognition
(1999-05-27)
Hong, WT
;
Chen, SH
;
電信工程研究所
;
Institute of Communications Engineering
An RNN-based robust signal bias removal (RRSBR) method is proposed for improving both the recognition performance and the computational efficiency of the SBR method fbr adverse Mandarin speech recognition. It differs from the SBR method in using three broad-class sub-codebooks to encode the feature vector of each frame and combining the three encoding residuals to form the frame-level signal bias estimate. A novel approach involving softly combining the board-class encoding residuals using dynamic weighting functions generated by an RNN is applied. Experimental results show that the RRSBR method significantly outperforms the SBR method.