Browsing by Author "Liao, Yuan-Fu"

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A GENERATIVE AUDITORY MODEL EMBEDDED NEURAL NETWORK FOR SPEECH PROCESSING
(2018-01-01)
Lo, Yu-Wen
;
Shen, Yih-Liang
;
Liao, Yuan-Fu
;
Chi, Tai-Shih
;
電機工程學系
Before the era of the neural network (NN), features extracted from auditory models have been applied to various speech applications and been demonstrated more robust against noise than conventional speech-processing features. What's the role of auditory models in the current NN era? Are they obsolete? To answer this question, we construct a NN with a generative auditory model embedded to process speech signals. The generative auditory model consists of two stages, the stage of spectrum estimation in the logarithmic-frequency axis by the cochlea and the stage of spectral-temporal analysis in the modulation domain by the auditory cortex. The NN is evaluated in a simple speaker identification task. Experiment results show that the auditory model embedded NN is still more robust against noise, especially in low SNR conditions, than the randomly-initialized NN in speaker identification.
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.
Distributed speech recognition of mandarin digits string
(2006)
Wang, Yih-Ru
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Lu, Bo-Xuan
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Liao, Yuan-Fu
;
Chen, Sin-Horng
;
交大名義發表
In this paper, the performance of the pitch detection algorithm in ETSI ES-202-212 XAFE standard is evaluated on. a Mandarin digit string recognition task. Experimental results showed that the performance of the pitch detection algorithm degraded seriously when the SNR of speech signal was lower than 10dB. This makes the recognizer using pitch information perform inferior to the original recognizer without using pitch information in low SNR environments. A modification of the pitch detection algorithm is therefore proposed to improve the performance of pitch detection in low SNR environments. The recognition performance can be improved for most SNR levels by integrating the recognizers with and without using pitch information. Overall recognition rates of 82.1% and 86.8% were achieved for clean and multi-condition training cases.
Evaluation of Weighted Graph and Neural Network Models on Predicting the Valence-Arousal Ratings of Chinese Words
(2016-01-01)
Chou, Wei-Chieh
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Lin, Chin-Kui
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Wang, Yih-Ru
;
Liao, Yuan-Fu
;
電機工程學系
Automatic sentiment information extraction of social network articles has many essential applications. Following the valence-arousal space framework, in this paper, two approaches including (1) a weighted graph (WG) and (2) a neural network (NN) model that could predict the valence-arousal ratings of words are evaluated on the Chinese valence-arousal words (CVAW) database provide by the IALP-2016 shared task. According the official evaluation results, our NN systems achieved (0.621,1.165) MAEs and (0.853,0.631) PCCs for valence and arousal predictions. Compared with the results of other participants, the performance of our systems are quite nice.
Latent prosody model of continuous mandarin speech
(2007)
Chiang, Chen-Yu
;
Wang, Xiao-Dong
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Liao, Yuan-Fu
;
Wang, Yih-Ru
;
Chen, Sin-Horng
The major difficulty of prosody modeling and automatic tone recognition of continuous Mandarin speech is the complex interaction of tones and prosody/intonation on F0 contours. In this study, we propose a latent prosody model (LPM) aiming to jointly model the affections of tone and prosody state on F0. The main purposes are twofold including (1) automatic prosody state labeling and (2) improving tone recognition accuracy. The basic idea is to introduce latent prosody state variables into an additive statistic model of F0 which already considers the affecting factors of tone and speaker. Experiments on the Tree-Bank corpus showed that LPM not only gave meaningful prosody state labeling results but also improved the average tone recognition rate from 80.86% of a multi-layer perceptron (MLP) baseline to 82.55%.
Modeling of Speaking Rate Influences on Mandarin Speech Prosody and Its Application to Speaking Rate-controlled TTS
(2014-07-01)
Chen, Sin-Horng
;
Hsieh, Chiao-Hua
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Chiang, Chen-Yu
;
Hsiao, Hsi-Chun
;
Wang, Yih-Ru
A new data-driven approach to building a speaking rate-dependent hierarchical prosodic model (SR-HPM), directly from a large prosody-unlabeled speech database containing utterances of various speaking rates, to describe the influences of speaking rate on Mandarin speech prosody is proposed. It is an extended version of the existing HPM model which contains 12 sub-models to describe various relationships of prosodic-acoustic features of speech signal, linguistic features of the associated text, and prosodic tags representing the prosodic structure of speech. Two main modifications are suggested. One is designing proper normalization functions from the statistics of the whole database to compensate the influences of speaking rate on all prosodic-acoustic features. Another is modifying the HPM training to let its parameters be speaking-rate dependent. Experimental results on a large Mandarin read speech corpus showed that the parameters of the SR-HPM together with these feature normalization functions interpreted the effects of speaking rate on Mandarin speech prosody very well. An application of the SR-HPM to design and implement a speaking rate-controlled Mandarin TTS system is demonstrated. The system can generate natural synthetic speech for any given speaking rate in a wide range of 3.4-6.8 syllables/sec. Two subjective tests, MOS and preference test, were conducted to compare the proposed system with the popular HTS system. The MOS scores of the proposed system were in the range of 3.58-3.83 for eight different speaking rates, while they were in 3.09-3.43 for HTS. Besides, the proposed system had higher preference scores (49.8%-79.6%) than those (9.8%-30.7%) of HTS. This confirmed the effectiveness of the speaking rate control method of the proposed TTS system.
Phone Boundary Detection Using Sample-Based Acoustic Parameters
(2010)
Lin, You-Yu
;
Wang, Yih-Ru
;
Liao, Yuan-Fu
;
傳播研究所
;
Institute of Communication Studies
A sample-based phone boundary detection algorithm is proposed in this paper. Some sample-based acoustic parameters are first extracted in the proposed method, including six sub-band signal envelopes, sample-based KL distance and spectral entropy. Then, the sample-based KL distance is used for boundary candidates pre-selection. Last, a supervised neural network is employed for final boundary detection. Experimental results using the TIMIT speech corpus showed that EERs of 13.2% and 15.1% were achieved for the training and test data sets, respectively. Moreover, 43.5% and 88.2% of boundaries detected were within 80- and 240-sample error tolerance from manual labeling results at the EER operating point.
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
;
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
;
Yang, Jyh-Her
;
Chen, Sin-Horng
;
電信工程研究所
;
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.