Browsing by Author "Hwang, Hsin-Te"

Now showing 1 - 9 of 9

A Hakka text-to-speech system
(2006)
Yu, Hsiu-Min
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Hwang, Hsin-Te
;
Lin, Dong-Yi
;
Chen, Sin-Horng
;
電信工程研究所
In this paper, the implementation of a Hakka text-to-speech (TIS) system is presented. The system is designed based on the same principle of developing a Mandarin and a Min-Nan TTS systems proposed previously. It takes 671 base-syllables as basic synthesis units and uses a recurrent neural network (RNN)-based prosody generator to generate proper prosodic parameters for synthesizing natural output speech. The whole system is implemented by software and runs in real-time on PC. Informal subjective listening test confirmed that the system performed well. All synthetic speeches sounded well for well-tokenized texts and fair for texts with automatic tokenization.
A Novel Model-based Pitch Conversion Method for Mandarin Speech
(2009)
Hwang, Hsin-Te
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Chiang, Chen-Yu
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Sung, Po-Yi
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Chen, Sin-Horng
;
電信工程研究所
In this paper, a novel model-based pitch conversion method for Mandarin is presented and compared with other two conventional conversion methods, i.e. the mean/variance transformation approach and the GMM-based mapping approach. Syllable pitch contour is first quantized by 3(rd) order orthogonal expansion coefficients; then, the source and target speakers' prosodic models are constructed, respectively. Two mapping methods based on the prosodic model are presented. Objective tests confirmed that one of the proposed methods are superior the conventional methods. Some findings in informal listening tests and objective tests are worthwhile to further investigate.
A Probabilistic Interpretation for Artificial Neural Network-based Voice Conversion
(2015)
Hwang, Hsin-Te
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Tsao, Yu
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Wang, Hsin-Min
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Wang, Yih-Ru
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Chen, Sin-Horng
Voice conversion (VC) using artificial neural networks (ANNs) has shown its capability to produce better sound quality of the converted speech than that using Gaussian mixture model (GMM). Although ANN-based VC works reasonably well, there is still room for further improvement. One of the promising ways is to adopt the successful techniques in statistical model-based parameter generation (SMPG), such as trajectory-based mapping approaches that are originally designed for GMM-based VC and hidden Markov model (HMM)-based speech synthesis. This study presents a probabilistic interpretation for ANN-based VC. In this way, ANN-based VC can easily incorporate the successful techniques in SMPG. Experimental results demonstrate that the performance of ANN-based VC can be effectively improved by two trajectory-based mapping techniques (maximum likelihood parameter generation (MLPG) algorithm and maximum likelihood-based trajectory mapping considering global variance (referred to as MLGV)), compared to the conventional ANN-based VC with frame-based mapping and the GMM-based VC with the MLPG algorithm. Moreover, ANN-based VC with the trajectory-based mapping techniques can achieve comparable performance when compared to the state-of-the-art GMM-based VC with the MLGV algorithm.
A Study of Mutual Information for GMM-Based Spectral Conversion
(2012)
Hwang, Hsin-Te
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Tsao, Yu
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Wang, Hsin-Min
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Wang, Yih-Ru
;
Chen, Sin-Horng
The Gaussian mixture model (GMM)-based method has dominated the field of voice conversion (VC) for last decade. However, the converted spectra are excessively smoothed and thus produce muffled converted sound. In this study, we improve the speech quality by enhancing the dependency between the source (natural sound) and converted feature vectors (converted sound). It is believed that enhancing this dependency can make the converted sound closer to the natural sound. To this end, we propose an integrated maximum a posteriori and mutual information (MAPMI) criterion for parameter generation on spectral conversion. Experimental results demonstrate that the quality of converted speech by the proposed MAPMI method outperforms that by the conventional method in terms of formal listening test.
Alleviating the Over-Smoothing Problem in GMM-Based Voice Conversion with Discriminative Training
(2013-01-01)
Hwang, Hsin-Te
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Tsao, Yu
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Wang, Hsin-Min
;
Wang, Yih-Ru
;
Chen, Sin-Horng
In this paper, we propose a discriminative training (DT) method to alleviate the muffled sound effect caused by over smoothing in the Gaussian mixture model (GMM)-based voice conversion (VC). For the conventional GMM-based VC, we often observed a large degree of ambiguities among acoustic classes (generative classes), determined by the source feature vectors for generating the converted feature vectors, causing the "muffled sound" effect on the converted voice. The proposed DT method is applied to refine the parameters in the maximum likelihood (ML)-trained joint density GMM (JDGMM) in the training stage to reduce the ambiguities among acoustic classes (generative classes) to alleviate the muffled sound effect. Experimental results demonstrate that the DT method significantly enhances the discriminative power between acoustic classes (generative classes) in the objective evaluation and effectively alleviates the muffled sound effect in the subjective evaluation.
EXPLORING MUTUAL INFORMATION FOR GMM-BASED SPECTRAL CONVERSION
(2012)
Hwang, Hsin-Te
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Tsao, Yu
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Wang, Hsin-Min
;
Wang, Yih-Ru
;
Chen, Sin-Horng
In this paper, we propose a maximum mutual information (MMI) training criterion to refine the parameters of the joint density GMM (JDGMM) set to tackle the over-smoothing issue in voice conversion (VC). Conventionally, the maximum likelihood (ML) criterion is used to train a JDGMM set, which characterizes the joint property of the source and target feature vectors. The MMI training criterion, on the other hand, updates the parameters of the JDGMM set to increase its capability on modeling the dependency between the source and target feature vectors, and thus to make the converted sounds closer to the natural ones. The subjective listening test demonstrates that the quality and individuality of the converted speech by the proposed ML followed by MMI (ML+MMI) training method is better that by the ML training method.
Incorporating Global Variance in the Training Phase of GMM-based Voice Conversion
(2013)
Hwang, Hsin-Te
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Tsao, Yu
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Wang, Hsin-Min
;
Wang, Yih-Ru
;
Chen, Sin-Horng
Maximum likelihood-based trajectory mapping considering global variance (MLGV-based trajectory mapping) has been proposed for improving the quality of the converted speech of Gaussian mixture model-based voice conversion (GMM-based VC). Although the quality of the converted speech is significantly improved, the computational cost of the online conversion process is also increased because there is no closed form solution for parameter generation in MLGV-based trajectory mapping, and an iterative process is generally required. To reduce the online computational cost, we propose to incorporate GV in the training phase of GMM-based VC. Then, the conversion process can simply adopt ML-based trajectory mapping (without considering GV in the conversion phase), which has a closed form solution. In this way, it is expected that the quality of the converted speech can be improved without increasing the online computational cost. Our experimental results demonstrate that the proposed method yields a significant improvement in the quality of the converted speech comparing to the conventional GMM-based VC method. Meanwhile, comparing to MLGV-based trajectory mapping, the proposed method provides comparable converted speech quality with reduced computational cost in the conversion process.
Locally Linear Embedding Based Post-Filtering for Speech Enhancement
(2018-11-01)
Hwang, Hsin-Te
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Wu, Yi-Chiao
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Wang, Syu-Siang
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Hsu, Chin-Cheng
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Tsao, Yu
We present a novel speech enhancement method based on locally linear embedding (LLE). The proposed method works as a post-filter to further suppress the residual noises in the enhanced speech signals obtained by a speech enhancement system to attain improved speech quality and intelligibility. We design two types of LLE-based post-filters: the direct LLE-based post-filter (called the DL post-filter) and the LLE-based difference compensation post-filter (called the LDC post-filter). The key technique of the proposed post-filters is to apply the LLE-based feature prediction method, which integrates the LLE algorithm, a classical manifold learning method, with the exemplar-based feature prediction method, to predict either the spectral features of the clean speech from those of the enhanced speech (for DL) or the spectral difference of {clean speech; noisy speech} from that of {enhanced speech; noisy speech} (for LDC). As a result, for DL, the predicted clean speech signals can be directly reconstructed from the predicted clean spectral features. On the other hand, for LDC, the predicted clean spectral features are obtained by compensating the spectral features of the noisy speech with the predicted clean-noisy spectral difference, and then the predicted clean speech signals can be reconstructed accordingly. Experimental results demonstrate the effectiveness of the proposed post-filters for two representative speech enhancement methods, namely the deep denoising autoencoder (DDAE) and the minimum mean-square-error (MMSE) spectral estimation methods.
Voice Conversion Based on Locally Linear Embedding
(2018-11-01)
Hwang, Hsin-Te
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Wu, Yi-Chiao
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Peng, Yu-Huai
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Hsu, Chin-Cheng
;
Tsao, Yu
This paper presents a novel locally linear embedding (LLE)-based framework for exemplar -based spectral conversion (SC). The key feature of the proposed SC framework is that it integrates the LLE algorithm, a manifold learning method, with the conventional exemplar -based SC method. One important advantage of the LLE-based SC framework is that it can be applied to either one-to-one SC or many-to-one SC. For one-to-one SC, a parallel speech corpus consisting of the pre-specified source and target speakers' speeches is used to construct the paired source and target dictionaries in advance. During online conversion, the LLE-based SC method converts the source spectral features to the target like spectral features based on the paired dictionaries. On the other hand, when applied to many-to-one SC, our system is capable of converting the voice of any unseen source speaker to that of a desired target speaker, without the requirement of collecting parallel training speech utterances from them beforehand. To further improve the quality of the converted speech, the maximum likelihood parameter generation (MLPG) and global variance (GV) methods are adopted in the proposed SC systems. Experimental results demonstrate that the proposed one-to-one SC system is comparable with the state-of-the-art Gaussian mixture model (GMM)-based one-to-one SC system in terms of speech quality and speaker similarity, and the many-to-one SC system can approximate the performance of the one-to-one SC system.