Browsing by Author "Liu, Ming-Chieh"

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A New Model-based Mandarin-speech Coding System
(2011-01-01)
Chiang, Chen-Yu
;
Yang, Jyh-Her
;
Liu, Ming-Chieh
;
Wang, Yih-Ru
;
Liao, Yuan-Fu
In this paper, a new model-based Mandarin-speech coding system is proposed. It employs a prosody-enriched ASR with a hierarchical prosodic model (HPM) to generate from the input speech enriched transcriptions, including linguistic features, prosodic tags and spectral parameters in the encoder. By sending these features to the decoder, we can first reconstruct the prosodic-acoustic features of syllable pitch contour, syllable duration, syllable energy level, and inter-syllable pause duration by HPM using the linguistic features and prosodic tags; and then combined with spectral parameters to reconstruct the input speech signal by an HMM-based speech synthesizer. Experimental results show that the reconstructed speech has good quality at a low data rate of 543 bits/s.
A New Prosody-Assisted Mandarin ASR System
(2012-08-01)
Chen, Sin-Horng
;
Yang, Jyh-Her
;
Chiang, Chen-Yu
;
Liu, Ming-Chieh
;
Wang, Yih-Ru
This paper presents a new prosody-assisted automatic speech recognition (ASR) system for Mandarin speech. It differs from the conventional approach of using simple prosodic cues on employing a sophisticated prosody modeling approach based on a four-layer prosody-hierarchy structure to automatically generate 12 prosodic models from a large unlabeled speech database by the joint prosody labeling and modeling (PLM) algorithm proposed previously. By incorporating these 12 prosodic models into a two-stage ASR system to rescore the word lattice generated in the first stage by the conventional hidden Markov model (HMM) recognizer, we can obtain a better recognized word string. Besides, some other information can also be decoded, including part of speech (POS), punctuation mark (PM), and two types of prosodic tags which can be used to construct the prosody-hierarchy structure of the testing speech. Experimental results on the TCC300 database, which consists of long paragraphic utterances, showed that the proposed system significantly outperformed the baseline scheme using an HMM recognizer with a factored language model which models word, POS, and PM. Performances of 20.7%, 14.4%, and 9.6% in word, character, and base-syllable error rates were obtained. They corresponded to 3.7%, 3.7%, and 2.4% absolute (or 15.2%, 20.4%, and 20% relative) error reductions. By an error analysis, we found that many word segmentation errors and tone recognition errors were corrected.
ENRICHING MANDARIN SPEECH RECOGNITION BY INCORPORATING A HIERARCHICAL PROSODY MODEL
(2011)
Yang, Jyh-Her
;
Liu, Ming-Chieh
;
Chang, Hao-Hsiang
;
Chiang, Chen-Yu
;
Wang, Yih-Ru
This paper presents a new probabilistic framework of Mandarin speech recognition by incorporating a sophisticated hierarchical prosody model into the conventional HMM-based system. The prosody model describes the relations of linguistic cues of various levels, break types and prosodic states which represent the prosody hierarchical structure, and prosody-related acoustic features. Aside from producing the recognized word sequences, the system also decodes other information including word's part-of-speech, punctuation marks, inter-syllable break types, and prosodic states of syllables. Experimental results on the TCC300 corpus, which consists of paragraphic utterances, showed that the proposed system significantly outperformed the baseline system. The word and character error rates decreased from 24.4% and 18.1% to 20.7% and 14.4% (or 15.2% and 20.4% relative improvements), respectively.