一种有效的语音信号盲分离方法

利用语音信号的短时平稳特性,本文提出了一种WVA分布与联合对角化的盲分离方法,该方法采用新的联合差分相关矩阵白化算法去除有色噪声影响,估计出源语音信号,实现对混叠信号的盲分离.通过仿真实验,结果表明,本算法具有分离效果好,能有效的将混叠的盲语音信号分离.     

语音信号盲分离方法研究

本文采用WVA分布与联合对角化的盲分离算法,估计出源语音信号,实现对混叠信号的盲分离。通过仿真实验,结果表明,本算法具有分离效果好,能有效的将混叠的盲语音信号分离。     

Multi-model approach for noisy speech recognition

A multi-model approach for noisy speech recognition is proposed. This approach comprised an SVD-based preprocessing front-end and a multi-model HMM recognition structure. It can provide a high recognition rate over a large range of SNRs for speech recognition in wide-band additive noise     

Nonlinear cepstral equalisation method for noisy speech recognition

The authors deal with the problem of automatic speech recognition in the presence of additive white noise. The effect of noise is modelled as an additive term to the power spectrum of the original clean speech. The cepstral coefficients of the noisy speech are then derived from this model. The reference cepstral vectors trained from clean speech are adapted to their appropriate noisy version to best fit the testing speech cepstral vector. The LPC coefficients, LPC derived cepstral coefficients, and the distance between test and reference, are all regarded as functions of the noise ratio (the spectral power ratio of noise to noisy speech). A gradient based algorithm is proposed to find the optimal noise ratio as well as the minimum distance between the test cepstral vector and the noise adapted reference. A recursive algorithm based on Levinson-Durbin recursion is proposed to simultaneously calculate the LPC coefficients and the derivatives of the LPC coefficients with respect to the noise ratio. The stability of the proposed adaptation algorithm is also addressed. Experiments on multispeaker (50 males and 50 females) isolated Mandarin digits recognition demonstrate remarkable performance improvements over noncompensated method under noisy environment. The results are also compared to the projection based approach, and experiments show that the proposed method is superior to the projection approach under a severe noisy environment     

噪声下差分复合子带语音识别方法

本文根据子带特征反映语音信号局部特性和全带特征反映语音信号整体特性的事实，提出了 一种差分复合子带语音识别新方法。先用频谱差分减少噪声的干扰，再将多子带特征识别概率与全带特征识别概率相结合进行综合判决，以得到最终识别结果。将新方法应用于TIMIT数据包0－9十个英文数字和E－Set在NoiseX92的白噪声和F16战机噪声下的识别实验。实验结果表明新方法比传统方法识别性能有很大提高。     

基于SVM的语音信号情感识别

在智能人-机交互系统中,语音信号的情感分类是目前热点的研究领域,并且得到了广泛的应用.本文提出一种基于特征提取和借助支持向量机(support vector machine,SVM)分类器(classifier)的情感互相关性的方法,并应用于情感语音识别.利用这种方法对3种情感语音信号进行情感分类.SVM分类器是利用情感语音信号中情感互相关性的特征提取进行分类的.这种通过 SVM 分类器的情感互相关性的自动分类方法,可以将情感识别率大幅提高,并且在识别愤怒情感时的准确率可以达到95.04%.     

弱稀疏语音信号的欠定盲分离

为解决弱稀疏语音信号的欠定盲分离问题,根据语音信号的部分W-分离正交性,提出一种基于单源主导区间的混合矩阵盲估计方法。该方法根据单源主导区间的性质,通过二元行矢量提取单源观测样本,对单源观测样本进行K均值聚类和主成分分析来估计混合矩阵。仿真结果表明,提出的方法可有效提高分离语音的性能,与直接利用K-PCA方法相比,分离语音的平均信噪比提高了10 dB左右。     

Blind separation of convolutive mixtures and an application inautomatic speech recognition in a noisy environment

We propose a two-step algorithm for the blind separation of convolutive mixtures. We discuss its application to automatic speech recognition in a noisy environment where the acoustic signals have been recorded by a microphone in a room whose furniture and walls produce echoes. The method yields good results     

一种用于抗噪语音识别的动态参数补偿新方法

模型补偿技术已成功应用到噪声环境下的语音识别任务中。流行的模型补偿技术如Log-Add和Log-Normal PMC（并行模型合并）方法对动态特征参数通常只能给出近似的补偿。因此他们的识别率在较低的信噪比条件下变得很低。本文利用静态特征的导函数推导出了一种新的动态模型参数补偿方法。新的方法可以同任何已知的静态模型补偿算法结合产生出新的用于识别的噪声语音模型。实验证明这一新算法的应用,使其识别率比仅使用原有的模型补偿算法有较为明显的提高,并且新算法的复杂度较原有的模型补偿算法只有轻微的增加。     

噪声环境下畸变模型线性化处理的顽健语音识别方法

针对噪声环境下语音识别的顽健性问题,考虑到梅尔倒谱系数(MFCC, Mel-frequency cepstral coefficient)域的畸变模型高度非线性且难以处理,用分段线性插值函数代替对数函数,提出了一种新的线性畸变模型.在此基础上,导出了噪声参数估计和声学模型补偿方法,无需采用矢量泰勒级数(VTS, vector Taylor series)展开作近似处理,有效避免了模型误差的引入,增强了系统在噪声环境下的顽健性.     

A blind source separation based method for speech encryption

The underdetermined problem poses a significant challenge in blind source separation (BSS) where the number of the source signals is greater than that of the mixed signals. Motivated by the fact that the security of many cryptosystems relies on the apparent intractability of the computational problems such as the integer factorization problem, we exploit the intractability of the underdetermined BSS problem to present a novel BSS-based speech encryption by properly constructing the underdetermined mixing matrix for encryption, and by generating the key signals that satisfy the necessary condition for the proposed method to be unconditionally secure. Both extensive computer simulations and performance analyses results show that the proposed method has high level of security while retaining excellent audio quality.     

Myoelectric signal classification for phoneme-based speech recognition

Traditional acoustic speech recognition accuracies have been shown to deteriorate in highly noisy environments. A secondary information source is exploited using surface myoelectric signals (MES) collected from facial articulatory muscles during speech. Words are classified at the phoneme level using a hidden Markov model (HMM) classifier. Acoustic and MES data was collected while the words "zero" through "nine" were spoken. An acoustic expert classified the 18 formative phonemes in low noise levels [signal-to-noise ratio (SNR) of 17.5 dB] with an accuracy of 99%, but deteriorated to approximately 38% under simulations with SNR approaching 0 dB. A fused acoustic-myoelectric multiexpert system, without knowledge of SNR, improved on acoustic classification results at all noise levels. A multiexpert system, incorporating SNR information, obtained accuracies of 99% at low noise levels while maintaining accuracies above 94% during low SNR (0 dB) simulations. Results improve on previous full word MES speech recognition accuracies by almost 10%.     

一种新的基于频域独立成分分析的语音信号盲分离方法

在频域利用传统的ICA进行分离时,如果分离矩阵没有经过良好的初始化,算法的收敛与分离性能都不够理想。本文提出了一种新的基于频域独立成分分析(ICA)的语音信号盲分离方法。首先通过分析混合信号的时频域特性对各个频带的分离矩阵进行初始化,使算法的收敛速度更快,并很好的解决了输出信号的次序不确定性问题;进一步根据以初始化的分离矩阵分离出的源信号间的幅度相关性,仅挑选出一部分频带进行ICA的迭代,最终达到在追求良好分离性能的同时极大提升运算效率的目的。仿真的无回声环境和几种实际的回声环境下所得到的实验结果表明,该方法在分离性能和算法效率上均优于传统的频域ICA方法。     

A time-frequency blind separation method for underdetermined speech mixtures

The proposed Blind Source Separation method （BSS）, based on sparse representations, fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources. The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique （DUET） which assumes the number of sources a priori. In the proposed algorithm, the Short-Time Fourier Transform （STFT） is used to obtain the sparse representations, a clustering method called Unsupervised Robust C-Prototypes （URCP） which can accurately identify multiple clusters regardless of the number of them is adopted to replace the histogram-based technique in DUET, and the binary time-frequency masks are constructed to separate the mixtures. Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio （SIR）, and maintains good speech quality in the separation results.     

A blind signal separation method for multiuser communications

A new approach based on the constant modulus (CM) criterion is proposed to separate instantaneous linear mixtures of signals using a linear memoryless multiple input multiple output (MIMO) system. Even though a nonconvex cost function is minimized, analyses show that minima correspond to parameter settings where perfect separation is achieved     

Mandarin speech recognition using segment-based cepstral comparisonin noisy conditions

A new scheme is proposed that compensates for the effects of noise in speech recognition systems. The new scheme was applied to Mandarin speech recognition. Another scheme, based on interpolation of the compensation vectors of several environments for a particular environment that is not obtained during the training phase, called interpolated SSDCN (ISSDCN), is also presented. Experimental results show that the scheme performs well under different SNR conditions     

High-speed parallel particle filter for PCMA signal blind separation

The paper proposes an improved high-speed parallel particle filter algorithm for the blind separation of PCMA-signals by utilizing particle filter’s characteristics of parallelism with the help of a cluster computer system built by using the Matlab distributed computing server and Matlab parallel computing toolbox. The simulation results show that the parallel algorithm can perform the PCMA-signal blind separation quickly and effectively. Further, it can greatly decrease the time of the separation, without reducing the performance of the algorithm, and improve the real-time application of system.     

一种强混响环境下的盲语音分离算法

强混响环境下语音信号的频域盲分离问题是盲源分离领域的一个难点,主要是因为混合系统的脉冲响应时间过长,甚至超过信号的非平稳时间,导致算法性能下降。本文针对这个问题提出了一种解决方法,在用一个短时傅立叶变换将时域卷积混合信号转化为频域的过程,再在时频域上使用另一个短时傅立叶变换,将信号变换到调制谱域,这样较长的脉冲响应就被转化为调制谱域上的瞬时混合形式,而瞬时混合情形则采用独立向量分析(IVA)算法来避免排序模糊性问题。计算机仿真实验证实了该算法在强混响环境下优于传统频域盲分离算法。      

A novel blind source separation method for single-channel signal

The blind separation of single-channel signal is one of the most important aspects in many fields. Our research is carried out to develop a blind separation method of single-channel signal, in which the singular spectrum analysis (SSA) and blind source separation (BSS) techniques are jointly used, i.e. the single-channel signal is firstly changed into pseudo-MIMO (multi-input and multi-output) mode, and then each source signal is separated via a fast BSS algorithm. A signal preprocessing procedure, which is mainly focused on testing the nonstationarity of single-channel signal, is conducted before the operations of mixed signal transform and separation. In this research, the approach of heuristic segmentation of a nonstationary time-series is proposed. Throughout the experiment, the effectiveness of the proposed method is validated with a data set taken from a digital wideband receiver in an outdoor test. Then, a comparison is made between the proposed method and the Hilbert–Huang transform (HHT)-based signal separation method. The advantage of the proposed method is exhibited.     

空间电子探测信号盲分离研究

给出了盲信号分离中的瞬时混合,时延混合和卷积混合三种混合模型,介绍了两种具体的盲分离算法,等变自适应盲分离算法和非高斯性最大化的快速定点算法.其中对于窄带源信号,对时延混合模型进行了扩展,提出了用复数域瞬时盲信号分离算法分离时延混合信号的新思路.最后给出了相应的仿真和实验结论,实验结果表明用基于复数的盲分离算法确实能够有效地分离阵列接收的时延混合信号.