Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Voice conversion using General Regression Neural Network

The objective of voice conversion system is to formulate the mapping function which can transform the source speaker characteristics to that of the target speaker. In this paper, we propose the General Regression Neural Network (GRNN) based model for voice conversion. It is a single pass learning network that makes the training procedure fast and comparatively less time consuming. The proposed system uses the shape of the vocal tract, the shape of the glottal pulse (excitation signal) and long term prosodic features to carry out the voice conversion task. In this paper, the shape of the vocal tract and the shape of source excitation of a particular speaker are represented using Line Spectral Frequencies (LSFs) and Linear Prediction (LP) residual respectively. GRNN is used to obtain the mapping function between the source and target speakers. The direct transformation of the time domain residual using Artificial Neural Network (ANN) causes phase change and generates artifacts in consecutive frames. In order to alleviate it, wavelet packet decomposed coefficients are used to characterize the excitation of the speech signal. The long term prosodic parameters namely, pitch contour (intonation) and the energy profile of the test signal are also modified in relation to that of the target (desired) speaker using the baseline method. The relative performances of the proposed model are compared to voice conversion system based on the state of the art RBF and GMM models using objective and subjective evaluation measures. The evaluation measures show that the proposed GRNN based voice conversion system performs slightly better than the state of the art models.     

Dynamic prosody modification using zero frequency filtered signal

Modifying the prosody parameters like pitch, duration and strength of excitation by desired factor is termed as prosody modification. The objective of this work is to develop a dynamic prosody modification method based on zero frequency filtered signal (ZFFS), a byproduct of zero frequency filtering (ZFF). The existing epoch based prosody modification techniques use epochs as pitch markers and the required prosody modification is achieved by the interpolation of epoch intervals plot. Alternatively, this work proposes a method for prosody modification by the resampling of ZFFS. Also the existing epoch based prosody modification method is further refined for modifying the prosodic parameters at every epoch level. Thus providing more flexibility for prosody modification. The general framework for deriving the modified epoch locations can also be used for obtaining the dynamic prosody modification from existing PSOLA and epoch based prosody modification methods. The quality of the prosody modified speech is evaluated using waveforms, spectrograms and subjective studies. The usefulness of the proposed dynamic prosody modification is demonstrated for neutral to emotional conversion task. The subjective evaluations performed for the emotion conversion indicate the effectiveness of the dynamic prosody modification over the fixed prosody modification for emotion conversion. The dynamic prosody modified speech files synthesized using the proposed, epoch based and TD-PSOLA methods are available at http://www.iitg.ac.in/eee/emstlab/demos/demo5.php.     

Prosody modification using instants of significant excitation

Prosody modification involves changing the pitch and duration of speech without affecting the message and naturalness. This paper proposes a method for prosody (pitch and duration) modification using the instants of significant excitation of the vocal tract system during the production of speech. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations like onset of burst in the case of nonvoiced speech. Instants of significant excitation are computed from the linear prediction (LP) residual of speech signals by using the property of average group-delay of minimum phase signals. The modification of pitch and duration is achieved by manipulating the LP residual with the help of the knowledge of the instants of significant excitation. The modified residual is used to excite the time-varying filter, whose parameters are derived from the original speech signal. Perceptual quality of the synthesized speech is good and is without any significant distortion. The proposed method is evaluated using waveforms, spectrograms, and listening tests. The performance of the method is compared with linear prediction pitch synchronous overlap and add (LP-PSOLA) method, which is another method for prosody manipulation based on the modification of the LP residual. The original and the synthesized speech signals obtained by the proposed method and by the LP-PSOLA method are available for listening at http://speech.cs.iitm.ernet.in/Main/result/prosody.html.     

基于HAAR小波的分级说话人辨识

从线性预测（LP）残差信号中提出了一种新的特征提取方法,这种特征跟单个的说话人的声道密切相关。通过把HAAR小波变换运用于LP 残差而获得了一个新的特征（HOCOR）。为了进一步提高系统的鲁棒性和辨识率,在采用分级说话人辨识的基础上,将基音周期的高斯概率密度对GMM分类器的似然度进行加权,形成新的似然度进行说话人辨识。试验结果显示,所提出系统的鲁棒性和辨识率都有所提高。     

Spotting glottal stop in Amharic in continuous speech

Glottal stop sounds in Amharic are produced due to abrupt closure of the glottis without any significant gesture in the accompanying articulatory organs in the vocal tract system. It is difficult to observe the features of the glottal stop through spectral analysis, as the spectral features emphasize mostly the features of the vocal tract system. In order to spot the glottal stop sounds in continuous speech, it is necessary to extract the features of the source of excitation also, which may require some non-spectral methods for analysis. In this paper the linear prediction (LP) residual is used as an approximation to the excitation source signal, and the excitation features are extracted from the LP residual using zero frequency filtering (ZFF). The glottal closure instants (GCIs) or epoch are identified from the ZFF signal. At each GCI, the cross-correlation coefficients of successive glottal cycles of the LP residual, the normalized jitter and the logarithm of the peak normalized excitation strength (LPNES) are calculated. Further, the parameters of Gaussian approximation models are derived from the distributions of the excitation parameters. These model parameters are used to identify the regions of the glottal stop sounds in continuous speech. For the database used in this study 92.89% of the glottal stop regions are identified correctly, with 8.50% false indications.     

Wavelet fuzzy LVQ based speaker verification system

This work evaluates the performance of speaker verification system based on Wavelet based Fuzzy Learning Vector Quantization (WLVQ) algorithm. The parameters of Gaussian mixture model (GMM) are designed using this proposed algorithm. Mel Frequency Cepstral Coefficients (MFCC) are extracted from the speech data and vector quantized through Wavelet based FLVQ algorithm. This algorithm develops a multi resolution codebook by updating both winning and nonwinning prototypes through an unsupervised learning process. This codebook is used as mean vector of GMM. The other two parameters, weight and covariance are determined from the clusters formed by the WLVQ algorithm. The multi resolution property of wavelet transform and ability of FLVQ in regulating the competition between prototypes during learning are combined in this algorithm to develop an efficient codebook for GMM. Because of iterative nature of Expectation Maximization (EM) algorithm, the applicability of alternative training algorithms is worth investigation. In this work, the performance of speaker verification system using GMM trained by LVQ, FLVQ and WLVQ algorithms are evaluated and compared with EM algorithm. FLVQ and WLVQ based training algorithms for modeling speakers using GMM yields better performance than EM based GMM.     

Performance of new voice conversion systems based on GMM models and applied to Arabic language

Voice conversion (VC) consists in modifying the source speaker’s voice toward the voice of the target speaker. In our paper, we are interested in calculating the performance of a conversion system based on GMM, applied to the Arabic language, by exploiting both the information of the pitch dynamics and the spectrum. We study three approaches to obtain the global conversion function of the pitch and the overall spectrum, using the joint probability model. In the first approach, we calculate the joint conversion of pitch and spectrum. In the second approach, the pitch is calculated by linear conversion. In the third approach, we use the relationship between the pitch and the spectrum. For the conversion of noise we use a new technique that consists in modeling the noise of the voiced or unvoiced frames by GMMs. We use the HNM for analysis/synthesis and a regularized discrete cepstrum in order to estimate the spectrum of the speech signal.     

Improvement of phone recognition accuracy using speech mode classification

In this work, we have developed a speech mode classification model for improving the performance of phone recognition system (PRS). In this paper, we have explored vocal tract system, excitation source and prosodic features for development of speech mode classification (SMC) model. These features are extracted from voiced regions of a speech signal. In this study, conversation, extempore, and read speech are considered as three different modes of speech. The vocal tract component of speech is extracted using Mel-frequency cepstral coefficients (MFCCs). The excitation source features are captured through Mel power differences of spectrum in sub-bands (MPDSS) and residual Mel-frequency cepstral coefficients (RMFCCs) of the speech signal. The prosody information is extracted from pitch and intensity. Speech mode classification models are developed using above described features independently, and in fusion. The experiments carried out on Bengali speech corpus to analyze the accuracy of the speech mode classification model using the artificial neural network (ANN), naive Bayes, support vector machines (SVMs) and k-nearest neighbor (KNN). We proposed four classification models which are combined using maximum voting approach for optimal performance. From the results, it is observed that speech mode classification model developed using the fusion of vocal tract system, excitation source and prosodic features of speech, yields the best performance of 98%. Finally, the proposed speech mode classifier is integrated to the PRS, and the accuracy of phone recognition system is observed to be improved by 11.08%.     

基于MFCC的频谱重构实现音高估计和发声分类

音高估计和发声分类可以帮助快速检索目标语音,是语音检索中十分重要且困难的研究方向之一,对语音识别领域具有重要的意义。提出了一种新型音高估计和发声分类方法。利用梅尔频率倒谱系数（MFCC）进行频谱重构,并在对数下对重构的频谱进行压缩和过滤。通过高斯混合模型（GMM）对音高频率和滤波频率的联合密度建模来实现音高估计,实验结果在TIMIT数据库上的相对误差为6.62%。基于高斯混合模型的模型也可以完成发声分类任务,经试验测试表明发声分类的准确率超过99%,为音高估计和发声分类提供了一种新的模型。     

Speaker verification using excitation source information

In this work we develop a speaker recognition system based on the excitation source information and demonstrate its significance by comparing with the vocal tract information based system. The speaker-specific excitation information is extracted by the subsegmental, segmental and suprasegmental processing of the LP residual. The speaker-specific information from each level is modeled independently using Gaussian mixture modeling—universal background model (GMM-UBM) modeling and then combined at the score level. The significance of the proposed speaker recognition system is demonstrated by conducting speaker verification experiments on the NIST-03 database. Two different tests, namely, Clean test and Noisy test are conducted. In case of Clean test, the test speech signal is used as it is for verification. In case of Noisy test, the test speech is corrupted by factory noise (9 dB) and then used for verification. Even though for Clean test case, the proposed source based speaker recognition system still provides relatively poor performance than the vocal tract information, its performance is better for Noisy test case. Finally, for both clean and noisy cases, by providing different and robust speaker-specific evidences, the proposed system helps the vocal tract system to further improve the overall performance.     

基于音素HMM模型语音转换

通过对语音转换的研究,提出了一种把源说话人特征转换为目标说话人特征的方法。语音转换特征参数分为两类：（1）频谱特征参数;（2）基音和声调模式。分别描述信号模型和转换方法。频谱特征用基于音素的2维HMMS建模,F0轨迹用来表示基音和音调。用基音同步叠加法对基音厨期、声调和语速进行变换。     

基于音素HMM模型语音转换

通过对语音转换的研究,提出了一种把源说话人特征转换为目标说话人特征的方法。语音转换特征参数分为两类:(1)频谱特征参数;(2)基音和声调模式。分别描述信号模型和转换方法。频谱特征用基于音素的2维HMMS建模,F0轨迹用来表示基音和音调。用基音同步叠加法对基音周期﹑声调和语速进行变换。     

基于GMM的说话人识别技术研究

为了探讨高斯混合模型在说话人识别中的作用,设计了一个基于GMM的说话人识别系统。整个系统由音频信号预处理,语音活动检测,说话人模型建立以及音频信号识别4个模块组成。前三个模块构成了系统的模型训练部分,最后一个模块构成了系统的语音识别部分。包含在第二个模块中的由GMM模型搭建的语音活动检测器是研究的创新之处。利用增强的多方互动会议语料库中的视听会议对系统中的部分可调参数以及系统的识别错误率进行了测试。仿真结果表明,在语音活动检测器和若干滤波算法的帮助下,系统对包含重叠语音的音频信号的识别准确率可以达到83.02%。     

基于基音周期与清浊音信息的梅尔倒谱参数

提出一种在浊音部分不固定帧长的梅尔倒谱参数(Mel-cepstrum)提取的方法。针对浊音和清音所包含信息量不同,对浊音进行双倍的加权,从而将基音与清浊音信息融合进梅尔倒谱参数。将这种动态的梅尔倒谱参数应用在说话人确认中,在混合高斯模型(Gaussian mixture models,GMM)的情况下,取得了比常用的梅尔刻度式倒频谱参数(Mel-frequency cepstral coefficient,MFCC)更高的识别率,在NIST 2002年测试数据库中,512个混合高斯下能够将等错误率(EER)由9.4%降低到8.3%,2 048个混合高斯下能够将等错误率由7.8%降低到6.9%。     

采用韵律特征的说话人确认系统

在文本无关的说话人识别中,韵律特征由于其对信道环境噪声不敏感等特性而被应用于话者识别任务中.本文对韵律参数采用基于高斯混合模型超向量的支持向量机建模方法,并将类内协方差特征映射方法应用于模型超向量上,单系统的性能比传统方法的混合高斯-通用背景模型(Gaussian mixture model-universal background model,GMM-UBM)基线系统有了40.19%的提升.该方法与本文的基于声学倒谱参数的确认系统融合后,能使整体系统的识别性能有9.25%的提升.在NIST(National institute of standards and technology mixture)2006说话人测试数据库上,融合后的系统能够取得4.9%的等错误率.     

Improving GMM–UBM speaker verification using discriminative feedback adaptation

The Gaussian mixture model – Universal background model (GMM–UBM) system is one of the predominant approaches for text-independent speaker verification, because both the target speaker model and the impostor model (UBM) have generalization ability to handle “unseen” acoustic patterns. However, since GMM–UBM uses a common anti-model, namely UBM, for all target speakers, it tends to be weak in rejecting impostors’ voices that are similar to the target speaker’s voice. To overcome this limitation, we propose a discriminative feedback adaptation (DFA) framework that reinforces the discriminability between the target speaker model and the anti-model, while preserving the generalization ability of the GMM–UBM approach. This is achieved by adapting the UBM to a target speaker dependent anti-model based on a minimum verification squared-error criterion, rather than estimating the model from scratch by applying the conventional discriminative training schemes. The results of experiments conducted on the NIST2001-SRE database show that DFA substantially improves the performance of the conventional GMM–UBM approach.     

同态解卷处理在基音检测中的应用

语音处理中基音检测是极为重要的环节之一,然而浊音中的基音往往会受到声道特性和噪声的影响而导致检测结果的误差。利用同态解卷处理,将浊音中的激励信号和声道特性进行分离,然后再在激励信号中利用自相关检测基音,可以减小声道特性和噪声对基音检测的影响,从而提高基音检测的精度。通过理论模型验证了该方法的可行性,而且实际语音信号处理结果表明,该方法在基音检测时可以基本不受声道特性和噪声的影响。     

Prosody conversion from neutral speech to emotional speech

Emotion is an important element in expressive speech synthesis. Unlike traditional discrete emotion simulations, this paper attempts to synthesize emotional speech by using "strong", "medium", and "weak" classifications. This paper tests different models, a linear modification model (LMM), a Gaussian mixture model (GMM), and a classification and regression tree (CART) model. The linear modification model makes direct modification of sentence F0 contours and syllabic durations from acoustic distributions of emotional speech, such as, F0 topline, F0 baseline, durations, and intensities. Further analysis shows that emotional speech is also related to stress and linguistic information. Unlike the linear modification method, the GMM and CART models try to map the subtle prosody distributions between neutral and emotional speech. While the GMM just uses the features, the CART model integrates linguistic features into the mapping. A pitch target model which is optimized to describe Mandarin F0 contours is also introduced. For all conversion methods, a deviation of perceived expressiveness (DPE) measure is created to evaluate the expressiveness of the output speech. The results show that the LMM gives the worst results among the three methods. The GMM method is more suitable for a small training set, while the CART method gives the better emotional speech output if trained with a large context-balanced corpus. The methods discussed in this paper indicate ways to generate emotional speech in speech synthesis. The objective and subjective evaluation processes are also analyzed. These results support the use of a neutral semantic content text in databases for emotional speech synthesis.     

Applied aspects of the synthesis and analysis of voice information

The authors present new results in solving problems of concatenative segment synthesis of voice information with prosody and vocal utterance, computer modeling of human voice signals based on joint models of human voice source and vocal tract, and speech signal preprocessing for automated documenting systems. The experiments show the efficiency of the proposed approaches.