基于环境特征判别学习的顽健语音识别方法

提出一种基于环境特征判别学习的顽健语音识别方法 ,它首先通过使用一个简单的分类器和梯度下降法迭代地学得环境特征 ,接着利用得到的环境特征从观测到的混噪语音特征中估计出纯净的语音特征 ,然后将估计出来的纯净语音特征用到后端的HMM分类器中 .使用所提出的方法对不特定话者小词表进行实验 ,其系统误识率与基本HMM系统相比下降了 33 3% .     

Noise‐Robust Speaker Recognition Using Subband Likelihoods and Reliable‐Feature Selection

We consider the feature recombination technique in a multiband approach to speaker identification and verification. To overcome the ineffectiveness of conventional feature recombination in broadband noisy environments, we propose a new subband feature recombination which uses subband likelihoods and a subband reliable‐feature selection technique with an adaptive noise model. In the decision step of speaker recognition, a few very low unreliable feature likelihood scores can cause a speaker recognition system to make an incorrect decision. To overcome this problem, reliable‐feature selection adjusts the likelihood scores of an unreliable feature by comparison with those of an adaptive noise model, which is estimated by the maximum a posteriori adaptation technique using noise features directly obtained from noisy test speech. To evaluate the effectiveness of the proposed methods in noisy environments, we use the TIMIT database and the NTIMIT database, which is the corresponding telephone version of TIMIT database. The proposed subband feature recombination with subband reliable‐feature selection achieves better performance than the conventional feature recombination system with reliable‐feature selection.     

A General Bayesian Model for Speaker Verification

This paper presents a general Bayesian model for speaker verification tasks.It is a generative probability model.Due to its simple analytical property,a computationally efficient expectation-maximization algorithm can be derived to obtain the model parameters.A closedform solution,which allows the scalable size of enrollment set,is given in a full Bayesian way for making speaker verification decisions.Factor analysis technique is employed to model the speaker-specific components,then the redundant information in this model will be dropped.Experimental results are evaluated by both equal error rate and minimum detection cost function.The proposed approach shows promising results on the National institute of standards and technology (NIST) Speaker recognition evaluation (SRE) 2010 extended and 2012 core tasks.Significant improvement is obtained when comparing with Gaussian probabilistic linear discriminant analysis,especially under phone-call conditions and mismatched train-test channel conditions.Contrast experimental results with other popular generative probability models are also presented in this paper.     

Simplification of I-Vector Extraction for Speaker Identification

The identity vector (i-vector) approach has been the state-of-the-art for text-independent speaker recognition,both identification and verification in recent years.An i-vector is a low-dimensional vector in the socalled total variability space represented with a thin and tall rectangular matrix.This paper introduces a novel algorithm to improve the computational and memory requirements for the application.In our method,the series of symmetric matrices can be represented by diagonal expression,sharing the same dictionary,which to some extent is analogous to eigen decomposition,and we name this algorithm Eigen decomposition like factorization (EDLF).Similar algorithms are listed for comparison,in the same condition,our method shows no disadvantages in identification accuracy.     

基于PDA平台的实时说话人识别系统

受到模型规模大和计算量大的限制,经典的基于高斯混合模型的说话人识别方法不适合于资源有限的PDA平台实时说话人自动识别要求。以Mel倒谱系数为说话人特征,运用主成分分类技术,结合定点数计算技术实现实时说话人自动识别。在19个用户的语音库上进行系统识别实验,此新型分类技术的训练时间缩短为基线系统的1/50,测试时间缩短为1/12,模型规模缩小为1/6,同时识别性能达到94.7%。     

噪声环境下说话人识别的组合特征提取方法

针对在干净语音环境下识别率很高的说话人识别系统,在噪声环境下识别率显著降低的缺点,本文结合具有多分辨率分析特点的小波变换技术,提出一种基于小波变换的组合特征提取算法,以提高说话人识别系统在噪声环境下的识别性能。对40个说话人的语音库SUDA2002-D2,在噪声环境下进行的识别实验结果表明,本文提出的组合特征提取算法可以在噪声环境下有效地提高说话人识别系统的识别性能。     

语音信号的加权mel倒谱分析

本文利用人耳的感知特性,提出了加权mel倒谱系数,并建立了相应的分析算法。实验结果表明,该系数不仅能够准确地刻画说话人声道的短时特征,还能用来重建出高质量的语音。因此加权mel倒谱分析不仅能够应用于语音识别和说话人识别,还能应用于语音编码和参数合成。     

SVM‐Based Speaker Verification System for Match‐on‐Card and Its Hardware Implementation

Using biometrics to verify a person's identity has several advantages over the present practice of personal identification numbers (PINs) and passwords. To gain maximum security in a verification system using biometrics, the computation of the verification as well as the storing of the biometric pattern has to take place in a smart card. However, there is an open issue of integrating biometrics into a smart card because of its limited resources (processing power and memory space). In this paper, we propose a speaker verification algorithm using a support vector machine (SVM) with a very few features, and implemented it on a 32‐bit smart card. The proposed algorithm can reduce the required memory space by a factor of more than 100 and can be executed in real‐time. Also, we propose a hardware design for the algorithm on a field‐programmable gate array (FPGA)‐based platform. Based on the experimental results, our SVM solution can provide superior performance over typical speaker verification solutions. Furthermore, our FPGA‐based solution can achieve a speed‐up of 50 times over a software‐based solution.     

基于不变集多小波的语音特征参数提取

在研究不变集多小波理论的基础上,借鉴Mel频率倒谱系数（MFCC）的提取算法,用多小波交换代替傅里叶变换及Mel滤波．构造了一种新的语音特征参数MWBC。汉语数字识别实验结果表明,提出的新语音特征参数MWBC的识别性能和抗噪性能均优于MFCC,为提高语音识别系统的噪声鲁棒性提供了一条新途径。     

基于区别特征检测的汉语韵母分类

从张家騄提出的韵母区别特征树出发,研究区别特征的声学参数.将韵母分为无介音和介音韵母两部分,使用支持向量机检测韵母的区别特征.在此基础上,通过区别特征树上的二元搜索过程实现韵母的分类.每个节点对应于一个区别特征,经过特定的搜索路径,韵母就被唯一确定下来.使用上述方法,大部分韵母的识别率在90％以上.     

用数字信号处理方法研究汉语普通话音素的区别特征

本文运用语言信号数字处理方法，研究了汉语普通话音素的区别特征，研究结果进一步完善了汉语普通话音素的区别特征矩阵表，将为基于音素的计算机汉语普通话语音分析、合成和识别提供了一种有效的参考方法。     

Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images

The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.     

一种鲁棒的语音信号基音标注算法

阐述了一种连续语音信号的最高振幅位的基音标注算法,该算法是在声带振动信号中找到喉部振动关闭点,并进行参数调整,从而自动完成在连续语音信号中的基音位置标注。实验证明,该算法能有效地克服以往标注算法中基音周期估计时加倍或减半的误差,从而保证了基音标注的鲁棒性。     

由局部能量确定可靠性加权的抗噪声语音识别方法

噪声情况下的语音识别是个挑战性的问题。目前的处理方法普遍需要估计噪声或者信噪比,从而其性能依赖于噪声估计的好坏。本文提出了一种基于语音信号局部能量的可靠性加权方法,该方法着眼于语音本身的结构,避免了对噪声的估计。另外,带噪语音识别的实验结果证明该方法能很好的提高识别系统的抗噪声性能。     

基于HMM的可训练情感语音合成研究

研究了基于HMM的英语语音合成系统,并通过分析情感语句的语调,得到高兴、生气、悲伤、平静四种不同情感类型的基音频率、能量和时长的变化规律。将基于HMM的可训练语音合成方法应用到英语情感语音合成,对基于HMM所合成的平静语音,叠加相应情感的语调变化规则,通过修改韵律特征参数合成出带有情感的语音。从所合成情感语句的效果来看,合成语音整体稳定流畅、情感特征明显,且合成系统可以快速构成。     

Class‐Based Histogram Equalization for Robust Speech Recognition

A new class‐based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class‐specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class‐specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel‐cepstral‐based features for test sets A, B, and C, respectively.     

基于最大似然子带线性回归的鲁棒语音识别

在实际环境中,训练环境和测试环境的失配会导致语音识别系统的性能急剧恶化.模型自适应算法是减小环境失配影响的有效方法之一,它通过少量自适应数据将模型参数变换到识别环境.最大似然线性回归是一种常用的基于变换的模型自适应算法,本文针对最大似然线性回归算法在数据较少时模型参数估计不准确的缺点,提出了基于最大似然子带线性回归的模型自适应算法.该算法将Mel滤波器组的全部通道划分为若干个子带,假设每个子带内多个通道的模型均值分量共享一个线性环境变换关系,以增加可用的数据.实验表明,本文算法可以较好地克服数据稀疏问题,只需要很少的数据即可取得较好的自适应效果,尤其适合于少量数据时的快速模型自适应.     

汉语语音识别的抗噪性前端算法及性能分析

讨论了欧洲电信标准委员会ETSI提出的分布式语音识别系统的抗噪前端特征提取算法,该算法融合多种抗噪技术。结合汉语语音的特点,进行了汉语语音识别整体框架下的算法实现,并进行了实验和分析,典型噪声环境下的识别结果证明,相对于基线MFCC特征提取算法,稳健性有较大提高。     

多模态生物识别中生物特征组合的最优化设计

如何在预算一定的情况下挑选出最佳的生物特征组合,通常是多模态生物特征识别技术中首先需要解决的问题。该文运用最优化技术中的“多目标规划”与“0-1线性整数规划”方法建立出一套简洁、实用的数学模型,并给出运用MATLAB的具体求解方法。经试验数据对比证明,该文提出的方法能在一定约束条件下快速、有效的找到满足用户要求的最优的多模态生物特征组合方案。     

用于噪声鲁棒语音识别的声学模型及解码策略

提出了一种平行子状态隐马尔可夫模型用作噪声鲁棒语音识别的声学模型。该模型融合了纯净语音和背景噪声信息,模型的每个状态包含平行关系的子状态。在此基础上,提出了两种用于平行子状态隐马尔可夫模型的识别解码策略——子状态最大似然解码和联合转移子状态最大似然解码。实验结果表明,声学模型及其解码策略在各种噪声下取得了良好鲁棒识别效果。