Browsing by Author "Yang, Jyh-Her"

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A New Model-based Mandarin-speech Coding System
(2011-01-01)
Chiang, Chen-Yu
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Yang, Jyh-Her
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Liu, Ming-Chieh
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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
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Yang, Jyh-Her
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Chiang, Chen-Yu
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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
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Liu, Ming-Chieh
;
Chang, Hao-Hsiang
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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.
Prosody-dependent Acoustic Modeling for Mandarin Speech Recognition
(2012)
Chiu, Tzu-Hsuan
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Chiang, Chen-Yu
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Liao, Yuan-Fu
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Yang, Jyh-Her
;
Wang, Yih-Ru
A study on introducing prosodic information to acoustic modeling (AM) for speech recognition is reported in this paper. It extends the conventional context-dependent (CD) triphone HMM modeling approach to further consider the dependency of phone model on the break type of nearby inter-syllable boundary. Four break types are considered, including major break, minor break, normal non-break, and tightly-coupled non-break. In the training phase, break labeling is automatically accomplished by a Prosody Labeling and Modeling algorithm proposed previously. Then, prosody-and phonetic-dependent phone models are constructed by a standard decision tree-based context clustering of HMMs. The effectiveness of the new AM was examined on a Mandarin syllable recognition task. Experimental results showed that the new approach outperformed the conventional CD-AM on achieving better syllable recognition rate as well as on obtaining a more efficient syllable lattice with better compromise on complexity verse syllable coverage rate.
SOFT-DECISION A PRIORI KNOWLEDGE INTERPOLATION FOR ROBUST TELEPHONE SPEAKER IDENTIFICATION
(2009)
Liao, Yuan-Fu
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Yang, Jyh-Her
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Chen, Sin-Horng
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電信工程研究所
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Institute of Communications Engineering
Handsets which are not seen in the training phase (a.k.a unseen handsets) are main sources of performance degradation for speaker identification (SID) applications in telecommunication environments. To alleviate the problem, a soft-decision a priori knowledge interpolation (SD-AKI) method of handset characteristic estimation for handset mismatch-compensated SID is proposed in this paper. The idea of the SD-AKI method is to first collect a set of characteristics of seen handsets in the training phase, and to then estimate the characteristic of the unknown testing handset by interpolating the set of seen handset characteristics in the test phase. The estimated handset characteristic is then used to compensate for handset mismatch for robust SID. The SD-AKI method can be realized in both feature and model spaces. Experimental results on the handset TIMIT (HTIMIT) database showed that both the proposed feature-and model-space SD-AKI schemes were more robust than the blind cepstral mean subtraction (CMS), feature warping (FW) methods and their hard-decision counterpart (HD-AKI) for both cases of all-handset and unseen-handset SID tests. It is therefore a promising robust SID method.
User Identification Design by Fusion of Face Recognition and Speaker Recognition
(2012-01-01)
Lin, Chao-Yu
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Song, Kai-Tai
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Chen, Yi-Wen
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Chien, Shuo-Cheng
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Chen, Sin-Horng
Intelligent human-robot interface helps a mobile robot to extract external information and interact with a user. User identification information allows a robot to generate appropriate behaviors and make personalized human-robot interaction (PHRI) more natural and safe. Most of service robots move around in various application settings, and biometric features always change during relative movements. In this work, a fusion recognition system based on face and speaker classifiers is designed to achieve the user recognition in various environments. We propose a method to fuse face and speech recognition results based on the concept of confidence indices. Through the derived confidence indices, the system combines the two classifiers and gives improved recognition results. Experiments on a laboratory home service robot show that person recognition rates in 15 environmental situations all outperform those of using single modality.