A voicing decision algorithm using continuous nonlinear network is discussed. A five dimensional feature vector is used to describe the voicing characteristic of speech segment, and a continuous network is trained wi...A voicing decision algorithm using continuous nonlinear network is discussed. A five dimensional feature vector is used to describe the voicing characteristic of speech segment, and a continuous network is trained with a gradient descent algorithm is served as the voicing decision maker. Computer simulation shows that this algorithm is an outperform way to make voicing decision. The correct rate of this method reaches 97.8%.展开更多
An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking met...An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.展开更多
A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-...A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02% respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.展开更多
Steganalysis can be used to classify an object whether or not it contains hidden information. In this article, is presented, a novel approach to detect the presence of least significant bit（LSB） steganographic messa...Steganalysis can be used to classify an object whether or not it contains hidden information. In this article, is presented, a novel approach to detect the presence of least significant bit（LSB） steganographic messages in the voice secure communication system. A distance measure, which has proven to be sensitive to LSB steganography by analysis of variance （ANOVA）, is denoted to estimate the difference between the host signal and the stego signal. Then an maximum likelihood （ML） decision is combined to form the classifier. Statistical experiments show that the proposed approach has a highly accurate rate and low computational complexity.展开更多
文摘A voicing decision algorithm using continuous nonlinear network is discussed. A five dimensional feature vector is used to describe the voicing characteristic of speech segment, and a continuous network is trained with a gradient descent algorithm is served as the voicing decision maker. Computer simulation shows that this algorithm is an outperform way to make voicing decision. The correct rate of this method reaches 97.8%.
基金Project supported by an Inha University Research GrantProject(10031764) supported by the Strategic Technology Development Program of Ministry of Knowledge Economy,Korea
文摘An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.
基金Supported by the Science and TechnologyCommittee of Shanghai (0 1JC14 0 3 3 )
文摘A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02% respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.
基金This work is supported by the Natural Science Foundation of Jiangsu Province（BK2004150）；the Hi-Tech Research and Development Program of China （2006AA010102）.
文摘Steganalysis can be used to classify an object whether or not it contains hidden information. In this article, is presented, a novel approach to detect the presence of least significant bit（LSB） steganographic messages in the voice secure communication system. A distance measure, which has proven to be sensitive to LSB steganography by analysis of variance （ANOVA）, is denoted to estimate the difference between the host signal and the stego signal. Then an maximum likelihood （ML） decision is combined to form the classifier. Statistical experiments show that the proposed approach has a highly accurate rate and low computational complexity.
