基于目标驱动的多层MLLR自适应算法

本文在对语音识别中基于自适应回归树的极大似然线性变换(MLLR)模型自适应算法深刻分析的基础上,提出了一种基于目标驱动的多层MLLR自适应(TMLLR)算法。这种算法基于目标驱动的原则,引入反馈机制,根据目标函数似然概率的增加来动态决定MLLR变换的变换类,大大提高了系统的识别率。并且由于这种算法的特殊多层结构,减少了许多中间的冗余计算,算法在具有较高的自适应精度的同时还具有较快的自适应速度。在有监督自适应实验中,经过此算法自适应后的系统识别率比基于自适应回归树的MLLR算法自适应后系统的误识率降低了10% ,自适应速度也比基于自适应回归树的MLLR算法快近一倍。     

Maximum likelihood stochastic transformation adaptation for medium and small data sets

Speaker adaptation is recognized as an essential part of today’s large-vocabulary automatic speech recognition systems. A family of techniques that has been extensively applied for limited adaptation data is transformation-based adaptation. In transformation-based adaptation we partition our parameter space in a set of classes, estimate a transform (usually linear) for each class and apply the same transform to all the components of the class. It is known, however, that additional gains can be made if we do not constrain the components of each class to use the same transform. In this paper two speaker adaptation algorithms are described. First, instead of estimating one linear transform for each class (as maximum likelihood linear regression (MLLR) does, for example) we estimate multiple linear transforms per class of models and a transform weights vector which is specific to each component (Gaussians in our case). This in effect means that each component receives its own transform without having to estimate each one of them independently. This scheme, termed maximum likelihood stochastic transformation (MLST) achieves a good trade-off between robustness and acoustic resolution. MLST is evaluated on the Wall Street Journal(WSJ) corpus for non-native speakers and it is shown that in the case of 40 adaptation sentences the algorithm outperforms MLLR by more than 13%. In the second half of this paper, we introduce a variant of the MLST designed to operate under sparsity of data. Since the majority of the adaptation parameters are the transformations, we estimate them on the training speakers and adapt to a new speaker by estimating the transform weights only. First we cluster the speakers in a number of sets and estimate the transformations on each cluster. The new speaker will use transformations from all clusters to perform adaptation. This method, termed basis transformation, can be seen as a speaker similarity scheme. Experimental results on the WSJ show that when basis transformation is cascaded with MLLR marginal gains can be obtained from MLLR only, for adaptation of native speakers.     

Mean and variance adaptation within the MLLR framework

One of the key issues for adaptation algorithms is to modify a large number of parameters with only a small amount of adaptation data. Speaker adaptation techniques try to obtain near speaker-dependent (SD) performance with only small amounts of speaker-specific data, and are often based on initial speaker-independent (SI) recognition systems. Some of these speaker adaptation techniques may also be applied to the task of adaptation to a new acoustic environment. In this case an SI recognition system trained in, typically, a clean acoustic environment is adapted to operate in a new, noise-corrupted, acoustic environment. This paper examines the maximum likelihood linear regression (MLLR) adaptation technique. MLLR estimates linear transformations for groups of model parameters to maximize the likelihood of the adaptation data. Previously, MLLR has been applied to the mean parameters in mixture-Gaussian HMM systems. In this paper MLLR is extended to also update the Gaussian variances and re-estimation formulae are derived for these variance transforms. MLLR with variance compensation is evaluated on several large vocabulary recognition tasks. The use of mean and variance MLLR adaptation was found to give an additional 2% to 7% decrease in word error rate over mean-only MLLR adaptation.     

Sub-vector based biometric speaker verification using MLLR super-vector

In this paper, we propose a sub-vector based speaker characterization method for biometric speaker verification, where speakers are represented by uniform segmentation of their maximum likelihood linear regression (MLLR) super-vectors called m-vectors. The MLLR transformation is estimated with respect to universal background model (UBM) without any speech/phonetic information. We introduce two strategies for segmentation of MLLR super-vector: one is called disjoint and other is an overlapped window technique. During test phase, m-vectors of the test utterance are scored against the claimant speaker. Before scoring, m-vectors are post-processed to compensate the session variability. In addition, we propose a clustering algorithm for multiple-class wise MLLR transformation, where Gaussian components of the UBM are clustered into different groups using the concept of expectation maximization (EM) and maximum likelihood (ML). In this case, MLLR transformations are estimated with respect to each class using the sufficient statistics accumulated from the Gaussian components belonging to the particular class, which are then used for m-vector system. The proposed method needs only once alignment of the data with respect to the UBM for multiple MLLR transformations. We first show that the proposed multi-class m-vector system shows promising speaker verification performance when compared to the conventional i-vector based speaker verification system. Secondly, the proposed EM based clustering technique is robust to the random initialization in-contrast to the conventional K-means algorithm and yields system performance better/equal which is best obtained by the K-means. Finally, we show that the fusion of the m-vector with the i-vector further improves the performance of the speaker verification in both score as well as feature domain. The experimental results are shown on various tasks of NIST 2008 speaker recognition evaluation (SRE) core condition.     

Aggregate a posteriori linear regression adaptation

We present a new discriminative linear regression adaptation algorithm for hidden Markov model (HMM) based speech recognition. The cluster-dependent regression matrices are estimated from speaker-specific adaptation data through maximizing the aggregate a posteriori probability, which can be expressed in a form of classification error function adopting the logarithm of posterior distribution as the discriminant function. Accordingly, the aggregate a posteriori linear regression (AAPLR) is developed for discriminative adaptation where the classification errors of adaptation data are minimized. Because the prior distribution of regression matrix is involved, AAPLR is geared with the Bayesian learning capability. We demonstrate that the difference between AAPLR discriminative adaptation and maximum a posteriori linear regression (MAPLR) adaptation is due to the treatment of the evidence. Different from minimum classification error linear regression (MCELR), AAPLR has closed-form solution to fulfil rapid adaptation. Experimental results reveal that AAPLR speaker adaptation does improve speech recognition performance with moderate computational cost compared to maximum likelihood linear regression (MLLR), MAPLR, MCELR and conditional maximum likelihood linear regression (CMLLR). These results are verified for supervised adaptation as well as unsupervised adaptation for different numbers of adaptation data.     

说话人自适应技术在维吾尔语语音识别中的应用研究

该文针对维吾尔语说话人之间的发音差异会在一定程度上影响维吾尔语语音识别系统的性能这一情况研究了说话人自适应技术,将目前较为常用的MLLR和MAP以及MLLR和MAP相结合的自适应方法应用于维吾尔语连续语音识别的声学模型训练中,并用这三种方法自适应后的声学模型分别在测试集上进行识别实验。实验结果表明MLLR、MAP以及MAP+MLLR自适应方法使基线识别系统的单词错误识别率分别降低了0.6%、2.34%和2.57%。
     

基于MAP和MLLR的综合渐进自适应方法研究

从说活人自适应技术讨论了最大后验概率(MAP)和最大似然线性回归(MLLR)两种经典的说活人自适应方法,通过在渐进的MAP方法中引入一个简化的MLLR模块,提出一种适合于强健语音识别的快速综合渐进自适应语音识别方法和策略。     

A study on the roles of total variability space and session variability modeling in speaker recognition

Speaker verification (SV) using i-vector concept becomes state-of-the-art. In this technique, speakers are projected onto the total variability space and represented by vectors called i-vectors. During testing, the i-vectors of the test speech segment and claimant are conditioned to compensate for the session variability before scoring. So, i-vector system can be viewed as two processing blocks: one is total variability space and the other is post-processing module. Several questions arise, such as, (i) which part of the i-vector system plays a major role in speaker verification: total variability space or post-processing task; (ii) is the post-processing module intrinsic to the total variability space? The motivation of this paper is to partially answer these questions by proposing several simpler speaker characterization systems for speaker verification, where speakers are represented by their speaker characterization vectors (SCVs). The SCVs are obtained by uniform segmentation of the speakers gaussian mixture models (GMMs)- and maximum likelihood linear regression (MLLR) super-vectors. We consider two adaptation approaches for GMM super-vector: one is maximum a posteriori and other is MLLR. Similarly to the i-vector, SCVs are post-processed for session variability compensation during testing. The proposed system shows promising performance when compared to the classical i-vector system which indicates that the post-processing task plays an major role in i-vector based SV system and is not intrinsic to the total variability space. All experimental results are shown on NIST 2008 SRE core condition.     

使用无监督网络MLLR自适应改进算法的语音识别

介绍了一种基于词网的最大似然线性回归(Lattice-MLLR)无监督自适应算法,并进行了改进。Lattice-MLLR是根据解码得到的词网估计MLLR变换参数,词网的潜在误识率远小于识别结果,因此可以使参数估计更为准确。Lattice-MLLR的一个很大缺点是计算量极大,较难实用,对此本文提出了两个改进技术:(1)利用后验概率压缩词网;(2)利用单词的时间信息限制状态统计量的计算范围。实验测定Lattice-MLLR的误识率比传统MLLR相对下降了3.5%,改进技术使Lattice-MLLR计算量下降幅度超过了87.9%。     

Improved automatic speech recognition through speaker normalization

In this paper, speaker adaptive acoustic modeling is investigated by using a novel method for speaker normalization and a well known vocal tract length normalization method. With the novel normalization method, acoustic observations of training and testing speakers are mapped into a normalized acoustic space through speaker-specific transformations with the aim of reducing inter-speaker acoustic variability. For each speaker, an affine transformation is estimated with the goal of reducing the mismatch between the acoustic data of the speaker and a set of target hidden Markov models. This transformation is estimated through constrained maximum likelihood linear regression and then applied to map the acoustic observations of the speaker into the normalized acoustic space.Recognition experiments made use of two corpora, the first one consisting of adults’ speech, the second one consisting of children’s speech. Performing training and recognition with normalized data resulted in a consistent reduction of the word error rate with respect to the baseline systems trained on unnormalized data. In addition, the novel method always performed better than the reference vocal tract length normalization method adopted in this work.When unsupervised static speaker adaptation was applied in combination with each of the two speaker normalization methods, a different behavior was observed on the two corpora: in one case performance became very similar while in the other case the difference remained significant.     

Incremental MLLR speaker adaptation by fuzzy logic control

This paper presents a fuzzy control mechanism for conventional maximum likelihood linear regression (MLLR) speaker adaptation, called FLC-MLLR, by which the effect of MLLR adaptation is regulated according to the availability of adaptation data in such a way that the advantage of MLLR adaptation could be fully exploited when the training data are sufficient, or the consequence of poor MLLR adaptation would be restrained otherwise. The robustness of MLLR adaptation against data scarcity is thus ensured. The proposed mechanism is conceptually simple and computationally inexpensive and effective; the experiments in recognition rate show that FLC-MLLR outperforms standard MLLR especially when encountering data insufficiency and performs better than MAPLR at much less computing cost.     

改进MCE训练算法在说话人识别中的应用

针对实际问题中训练数据不足的特点,在对说话人建模时采用的是高斯混合模型—通用背景模型GMM-UBM,针对MCE训练算法中计算量大的显著问题,对其进行改进,改进的MCE算法不仅能使计算量减小,而且识别性能更佳。实验结果表明,在高斯混合数与说话人数不同的情况下,改进的MCE比传统MCE算法都要节省训练时间,且随着高斯混合数与说话人数的增长,节省的时间越多。针对采用MAP、MLLR、MAP\MLLR、EigenVoice方法作自适应得到的说话人模型,然后应用MCE算法与改进的MCE算法,改进的MCE算法比传统MCE方法识别率更高。     

基于最大似然线性回归矩阵的说话人识别算法研究

研究了将自适应领域的最大似然线性回归(Maximum likelihood linear regression, MLLR)变换矩阵作为特征进行文本无关的说话人识别算法. 本文引入了基于统一背景模型的MLLRSV-SVM说话人识别算法, 并在此基础上进行高层音素聚类以进一步提高识别性能. 在采用多种信道补偿技术后, 在NIST SRE 2006年1训练语段-1测试语段同信道和跨信道数据库上, 基于MLLR特征的系统与其他最好的系统性能接近并有很强的互补性, 经过简单线性融合可以极大提高识别性能.     

Adaptation of children’s speech with limited data based on formant-like peak alignment

Automatic recognition of children’s speech using acoustic models trained by adults results in poor performance due to differences in speech acoustics. These acoustical differences are a consequence of children having shorter vocal tracts and smaller vocal cords than adults. Hence, speaker adaptation needs to be performed. However, in real-world applications, the amount of adaptation data available may be less than what is needed by common speaker adaptation techniques to yield reasonable performance. In this paper, we first study, in the discrete frequency domain, the relationship between frequency warping in the front-end and corresponding transformations in the back-end. Three common feature extraction schemes are investigated and their transformation linearity in the back-end are discussed. In particular, we show that under certain approximations, frequency warping of MFCC features with Mel-warped triangular filter banks equals a linear transformation in the cepstral space. Based on that linear transformation, a formant-like peak alignment algorithm is proposed to adapt adult acoustic models to children’s speech. The peaks are estimated by Gaussian mixtures using the Expectation-Maximization (EM) algorithm [Zolfaghari, P., Robinson, T., 1996. Formant analysis using mixtures of Gaussians, Proceedings of International Conference on Spoken Language Processing, 1229–1232]. For limited adaptation data, the algorithm outperforms traditional vocal tract length normalization (VTLN) and maximum likelihood linear regression (MLLR) techniques.     

Kernel Eigenspace-Based MLLR Adaptation

In this paper, we propose an application of kernel methods for fast speaker adaptation based on kernelizing the eigenspace-based maximum-likelihood linear regression adaptation method. We call our new method "kernel eigenspace-based maximum-likelihood linear regression adaptation" (KEMLLR). In KEMLLR, speaker-dependent (SD) models are estimated from a common speaker-independent (SI) model using MLLR adaptation, and the MLLR transformation matrices are mapped to a kernel-induced high-dimensional feature space, wherein kernel principal component analysis is used to derive a set of eigenmatrices. In addition, a composite kernel is used to preserve row information in the transformation matrices. A new speaker's MLLR transformation matrix is then represented as a linear combination of the leading kernel eigenmatrices, which, though exists only in the feature space, still allows the speaker's mean vectors to be found explicitly. As a result, at the end of KEMLLR adaptation, a regular hidden Markov model (HMM) is obtained for the new speaker and subsequent speech recognition is as fast as normal HMM decoding. KEMLLR adaptation was tested and compared with other adaptation methods on the Resource Management and Wall Street Journal tasks using 5 or 10 s of adaptation speech. In both cases, KEMLLR adaptation gives the greatest improvement over the SI model with 11%-20% word error rate reduction     

Rapid Speaker Adaptation Using Clustered Maximum-Likelihood Linear Basis With Sparse Training Data

Speaker space-based adaptation methods for automatic speech recognition have been shown to provide significant performance improvements for tasks where only a few seconds of adaptation speech is available. However, these techniques are not widely used in practical applications because they require large amounts of speaker-dependent training data and large amounts of computer memory. The authors propose a robust, low-complexity technique within this general class that has been shown to reduce word error rate, reduce the large storage requirements associated with speaker space approaches, and eliminate the need for large numbers of utterances per speaker in training. The technique is based on representing speakers as a linear combination of clustered linear basis vectors and a procedure is presented for maximum-likelihood estimation of these vectors from training data. Significant word error rate reduction was obtained using these methods relative to speaker independent performance for the Resource Management and Wall Street Journal task domains.     

Speaker Recognition With Session Variability Normalization Based on MLLR Adaptation Transforms

We present a new modeling approach for speaker recognition that uses the maximum-likelihood linear regression (MLLR) adaptation transforms employed by a speech recognition system as features for support vector machine (SVM) speaker models. This approach is attractive because, unlike standard frame-based cepstral speaker recognition models, it normalizes for the choice of spoken words in text-independent speaker verification without data fragmentation. We discuss the basics of the MLLR-SVM approach, and show how it can be enhanced by combining transforms relative to multiple reference models, with excellent results on recent English NIST evaluation sets. We then show how the approach can be applied even if no full word-level recognition system is available, which allows its use on non-English data even without matching speech recognizers. Finally, we examine how two recently proposed algorithms for intersession variability compensation perform in conjunction with MLLR-SVM.     

Adaptation of automatic speech recognizers to new speakers using canonical correlation analysis techniques

This paper describes various speaker normalization and adaptation techniques of a knowledge data base or reference templates to new speakers in automatic speech recognition (ASR). It focuses on a technique for learning spectral transformations, based on a statistical-analysis tool (canonical correlation analysis), to adapt a standard dictionary to arbitrary speakers. The proposed method should permit to improve speaker independence in large vocabulary ASR. Application to an isolated word recognizer improved a 70% correct score to 87%.A dynamic aspect of the speaker adaptation procedure is introduced and evaluated in a particular strategy.     

Uncertainty-based learning of acoustic models from noisy data

We consider the problem of acoustic modeling of noisy speech data, where the uncertainty over the data is given by a Gaussian distribution. While this uncertainty has been exploited at the decoding stage via uncertainty decoding, its usage at the training stage remains limited to static model adaptation. We introduce a new expectation maximization (EM) based technique, which we call uncertainty training, that allows us to train Gaussian mixture models (GMMs) or hidden Markov models (HMMs) directly from noisy data with dynamic uncertainty. We evaluate the potential of this technique for a GMM-based speaker recognition task on speech data corrupted by real-world domestic background noise, using a state-of-the-art signal enhancement technique and various uncertainty estimation techniques as a front-end. Compared to conventional training, the proposed training algorithm results in 3–4% absolute improvement in speaker recognition accuracy by training from either matched, unmatched or multi-condition noisy data. This algorithm is also applicable with minor modifications to maximum a posteriori (MAP) or maximum likelihood linear regression (MLLR) acoustic model adaptation from noisy data and to other data than audio.