Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial,spectral and temporal modeling of sounds

Research on noise robust speech recognition has mainly focused on dealing with relatively stationary noise that may differ from the noise conditions in most living environments. In this paper, we introduce a recognition system that can recognize speech in the presence of multiple rapidly time-varying noise sources as found in a typical family living room. To deal with such severe noise conditions, our recognition system exploits all available information about speech and noise; that is spatial (directional), spectral and temporal information. This is realized with a model-based speech enhancement pre-processor, which consists of two complementary elements, a multi-channel speech–noise separation method that exploits spatial and spectral information, followed by a single channel enhancement algorithm that uses the long-term temporal characteristics of speech obtained from clean speech examples. Moreover, to compensate for any mismatch that may remain between the enhanced speech and the acoustic model, our system employs an adaptation technique that combines conventional maximum likelihood linear regression with the dynamic adaptive compensation of the variance of the Gaussians of the acoustic model. Our proposed system approaches human performance levels by greatly improving the audible quality of speech and substantially improving the keyword recognition accuracy.     

Multi-environment model adaptation based on vector Taylor series for robust speech recognition

In this paper, we propose a multi-environment model adaptation method based on vector Taylor series (VTS) for robust speech recognition. In the training phase, the clean speech is contaminated with noise at different signal-to-noise ratio (SNR) levels to produce several types of noisy training speech and each type is used to obtain a noisy hidden Markov model (HMM) set. In the recognition phase, the HMM set which best matches the testing environment is selected, and further adjusted to reduce the environmental mismatch by the VTS-based model adaptation method. In the proposed method, the VTS approximation based on noisy training speech is given and the testing noise parameters are estimated from the noisy testing speech using the expectation-maximization (EM) algorithm. The experimental results indicate that the proposed multi-environment model adaptation method can significantly improve the performance of speech recognizers and outperforms the traditional model adaptation method and the linear regression-based multi-environment method.     

Approaches to Iterative Speech Feature Enhancement and Recognition

In automatic speech recognition, hidden Markov models (HMMs) are commonly used for speech decoding, while switching linear dynamic models (SLDMs) can be employed for a preceding model-based speech feature enhancement. In this paper, these model types are combined in order to obtain a novel iterative speech feature enhancement and recognition architecture. It is shown that speech feature enhancement with SLDMs can be improved by feeding back information from the HMM to the enhancement stage. Two different feedback structures are derived. In the first, the posteriors of the HMM states are used to control the model probabilities of the SLDMs, while in the second they are employed to directly influence the estimate of the speech feature distribution. Both approaches lead to improvements in recognition accuracy both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature enhancement with SLDMs. It is also shown that a combination with uncertainty decoding further enhances performance.      

Stereo hidden Markov modeling for noise robust speech recognition

This paper investigates a noise robust technique for automatic speech recognition which exploits hidden Markov modeling of stereo speech features from clean and noisy channels. The HMM trained this way, referred to as stereo HMM, has in each state a Gaussian mixture model (GMM) with a joint distribution of both clean and noisy speech features. Given the noisy speech input, the stereo HMM gives rise to a two-pass compensation and decoding process where MMSE denoising based on N-best hypotheses is first performed and followed by decoding the denoised speech in a reduced search space on lattice. Compared to the feature space GMM-based denoising approaches, the stereo HMM is advantageous as it has finer-grained noise compensation and makes use of information of the whole noisy feature sequence for the prediction of each individual clean feature. Experiments on large vocabulary spontaneous speech from speech-to-speech translation applications show that the proposed technique yields superior performance than its feature space counterpart in noisy conditions while still maintaining decent performance in clean conditions.     

一种改进的特征提取方法在语音识别中的应用

针对梅尔频率倒谱系数(MFCC)参数在噪声环境中语音识别率下降的问题,提出了一种基于耳蜗倒谱系数(CFCC)的改进的特征参数提取方法.提取具有听觉特性的CFCC特征参数;运用改进的线性判别分析(LDA)算法对提取出的特征参数进行线性变换,得到更具有区分性的特征参数和满足隐马尔可夫模型(HMM)需要的对角化协方差矩阵;进行均值方差归一化,得到最终的特征参数.实验结果表明:提出的方法能有效地提高噪声环境中语音识别系统的识别率和鲁棒性.     

Feature Enhancement for Noisy Speech Recognition With a Time-Variant Linear Predictive HMM Structure

This paper presents a new approach for speech feature enhancement in the log-spectral domain for noisy speech recognition. A switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution. Each multivariate linear dynamic model (LDM) is associated with the hidden state of a hidden Markov model (HMM) as an attempt to describe the temporal correlations among adjacent frames of speech features. The state transition on the Markov chain is the process of activating a different LDM or activating some of them simultaneously by different probabilities generated by the HMM. Rather than holding a transition probability for the whole process, a connectionist model is employed to learn the time variant transition probabilities. With the resulting SLDM as the speech model and with a model for the noise, speech and noise are jointly tracked by means of switching Kalman filtering. Comprehensive experiments are carried out using the Aurora2 database to evaluate the new algorithm. The results show that the new SLDM approach can further improve the speech feature enhancement performance in terms of noise-robust recognition accuracy, since the transition probabilities among the LDMs can be described more precisely at each time point.     

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.     

语音分离与HMM相结合的语音增强方法

针对基于隐马尔科夫（HMM,Hidden Markov Model）的MAP和MMSE两种语音增强算法计算量大且前者不能处理非平稳噪声的问题,借鉴语音分离方法,提出了一种语音分离与HMM相结合的语音增强算法。该算法采用适合处理非平稳噪声的多状态多混合单元HMM,对带噪语音在语音模型和噪声模型下的混合状态进行解码,结合语音分离方法中的最大模型理论进行语音估计,避免了迭代过程和计算量特别大的公式计算,减少了计算复杂度。实验表明,该算法能够有效地去除平稳噪声和非平稳噪声,且感知评价指标PESQ 的得分有明显提高,算法时间也得到有效控制。     

Environment dependent noise tracking for speech enhancement

Numerous efforts have focused on the problem of reducing the impact of noise on the performance of various speech systems such as speech recognition, speaker recognition, and speech coding. These approaches consider alternative speech features, improved speech modeling, or alternative training for acoustic speech models. This study presents an alternative viewpoint by approaching the same problem from the noise perspective. Here, a framework is developed to analyze and use the noise information available for improving performance of speech systems. The proposed framework focuses on explicitly modeling the noise and its impact on speech system performance in the context of speech enhancement. The framework is then employed for development of a novel noise tracking algorithm for achieving better speech enhancement under highly evolving noise types. The first part of this study employs a noise update rate in conjunction with a target enhancement algorithm to evaluate the need for tracking in many enhancement algorithms. It is shown that noise tracking is more beneficial in some environments than others. This is evaluated using the Log-MMSE enhancement scheme for a corpus of four noise types consisting of Babble (BAB), White Gaussian (WGN), Aircraft Cockpit (ACN), and Highway Car (CAR) using the Itakura-Saito (IS) (Gray et al. in IEEE Trans. Acoust. Speech Signal Process. 28:367–376, 1980) quality measure. A test set of 200 speech utterances from the TIMIT corpus are used for evaluations. The new Environmentally Aware Noise Tracking (EA-NT) method is shown to be superior in comparison with the contemporary noise tracking algorithms. Evaluations are performed for speech degraded using a corpus of four noise types consisting of: Babble (BAB), Machine Gun (MGN), Large Crowd (LCR), and White Gaussian (WGN). Unlike existing approaches, this study provides an effective foundation for addressing noise in speech by emphasizing noise modeling so that available resources can be used to achieve more reliable overall performance in speech systems.     

基于HMM和聚类的英语语音识别混合算法

对于具有大量特征数据和复杂发音变化的英语语音,与单词相比,在隐马尔可夫模型(HMM)中存在更多问题,例如维特比算法的复杂度计算和高斯混合模型中的概率分布问题。为了实现基于HMM和聚类的独立于说话人的英语语音识别系统,提出了用于降低语音特征参数维数的分段均值算法、聚类交叉分组算法和HMM分组算法的组合形式。实验结果表明,与单个HMM模型相比,该算法不仅提高了英语语音的识别率近3%,而且提高系统的识别速度20.1%。     

A unified framework of HMM adaptation with joint compensation of additive and convolutive distortions

In this paper, we present our recent development of a model-domain environment robust adaptation algorithm, which demonstrates high performance in the standard Aurora 2 speech recognition task. The algorithm consists of two main steps. First, the noise and channel parameters are estimated using multi-sources of information including a nonlinear environment-distortion model in the cepstral domain, the posterior probabilities of all the Gaussians in speech recognizer, and truncated vector Taylor series (VTS) approximation. Second, the estimated noise and channel parameters are used to adapt the static and dynamic portions (delta and delta–delta) of the HMM means and variances. This two-step algorithm enables joint compensation of both additive and convolutive distortions (JAC). The hallmark of our new approach is the use of a nonlinear, phase-sensitive model of acoustic distortion that captures phase asynchrony between clean speech and the mixing noise.In the experimental evaluation using the standard Aurora 2 task, the proposed Phase-JAC/VTS algorithm achieves 93.32% word accuracy using the clean-trained complex HMM backend as the baseline system for the unsupervised model adaptation. This represents high recognition performance on this task without discriminative training of the HMM system. The experimental results show that the phase term, which was missing in all previous HMM adaptation work, contributes significantly to the achieved high recognition accuracy.     

Static and Dynamic Variance Compensation for Recognition of Reverberant Speech With Dereverberation Preprocessing

The performance of automatic speech recognition is severely degraded in the presence of noise or reverberation. Much research has been undertaken on noise robustness. In contrast, the problem of the recognition of reverberant speech has received far less attention and remains very challenging. In this paper, we use a dereverberation method to reduce reverberation prior to recognition. Such a preprocessor may remove most reverberation effects. However, it often introduces distortion, causing a dynamic mismatch between speech features and the acoustic model used for recognition. Model adaptation could be used to reduce this mismatch. However, conventional model adaptation techniques assume a static mismatch and may therefore not cope well with a dynamic mismatch arising from dereverberation. This paper proposes a novel adaptation scheme that is capable of managing both static and dynamic mismatches. We introduce a parametric model for variance adaptation that includes static and dynamic components in order to realize an appropriate interconnection between dereverberation and a speech recognizer. The model parameters are optimized using adaptive training implemented with the expectation maximization algorithm. An experiment using the proposed method with reverberant speech for a reverberation time of 0.5 s revealed that it was possible to achieve an 80% reduction in the relative error rate compared with the recognition of dereverberated speech (word error rate of 31%), and the final error rate was 5.4%, which was obtained by combining the proposed variance compensation and MLLR adaptation.     

Personalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis

In this paper we present results of unsupervised cross-lingual speaker adaptation applied to text-to-speech synthesis. The application of our research is the personalisation of speech-to-speech translation in which we employ a HMM statistical framework for both speech recognition and synthesis. This framework provides a logical mechanism to adapt synthesised speech output to the voice of the user by way of speech recognition. In this work we present results of several different unsupervised and cross-lingual adaptation approaches as well as an end-to-end speaker adaptive speech-to-speech translation system. Our experiments show that we can successfully apply speaker adaptation in both unsupervised and cross-lingual scenarios and our proposed algorithms seem to generalise well for several language pairs. We also discuss important future directions including the need for better evaluation metrics.     

Noisy speech processing by recurrently adaptive fuzzy filters

Two noisy speech processing problems-speech enhancement and noisy speech recognition-are dealt with. The technique we focus on is by using the filtering approach; a novel filter, the recurrently adaptive fuzzy filter (RAFF), is proposed and applied to these two problems. The speech enhancement is based on adaptive noise cancellation with two microphones, where the RAFF is used to eliminate the noise corrupting the desired speech signal in the primary channel. As to the noisy speech recognition, the RAFF is used to filter the noise in the feature domain of speech signals. The RAFF is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. As compared to other existing nonlinear filters, three major advantages of the RAFF are observed: 1) a priori knowledge can be incorporated into the RAFF, which makes the fusion of numerical data and linguistic information possible; 2) owing to the dynamic property of the RAFF, the exact lagged order of the input variables need not be known in advance; 3) no predetermination, like the number of hidden nodes, must be given since the RAFF can find its optimal structure and parameters automatically Several examples on adaptive noise cancellation and noisy speech recognition problems using the RAFF are illustrated to demonstrate the performance of the RAFF     

Optimized discriminative transformations for speech features based on minimum classification error

Feature extraction is an important component of pattern classification and speech recognition. Extracted features should discriminate classes from each other while being robust to environmental conditions such as noise. For this purpose, several feature transformations are proposed which can be divided into two main categories: data-dependent transformation and classifier-dependent transformation. The drawback of data-dependent transformation is that its optimization criteria are different from the measure of classification error which can potentially degrade the classifier’s performance. In this paper, we propose a framework to optimize data-dependent feature transformations such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis) and HLDA (Heteroscedastic LDA) using minimum classification error (MCE) as the main objective. The classifier itself is based on Hidden Markov Model (HMM). In our proposed HMM minimum classification error technique, the transformation matrices are modified to minimize the classification error for the mapped features, and the dimension of the feature vector is not changed. To evaluate the proposed methods, we conducted several experiments on the TIMIT phone recognition and the Aurora2 isolated word recognition tasks. The experimental results show that the proposed methods improve performance of PCA, LDA and HLDA transformation for mapping Mel-frequency cepstral coefficients (MFCC).     

Phoneme class based feature adaptation for mismatch acoustic modeling and recognition of distant noisy speech

Distant speech capture in lecture halls and auditoriums offers unique challenges in algorithm development for automatic speech recognition. In this study, a new adaptation strategy for distant noisy speech is created by the means of phoneme classes. Unlike previous approaches which adapt the acoustic model to the features, the proposed phoneme-class based feature adaptation (PCBFA) strategy adapts the distant data features to the present acoustic model which was previously trained on close microphone speech. The essence of PCBFA is to create a transformation strategy which makes the distributions of phoneme-classes of distant noisy speech similar to those of a close talk microphone acoustic model in a multidimensional MFCC space. To achieve this task, phoneme-classes of distant noisy speech are recognized via artificial neural networks. PCBFA is the adaptation of features rather than adaptation of acoustic models. The main idea behind PCBFA is illustrated via conventional Gaussian mixture model–Hidden Markov model (GMM–HMM) although it can be extended to new structures in automatic speech recognition (ASR). The new adapted features together with the new and improved acoustic models produced by PCBFA are shown to outperform those created only by acoustic model adaptations for ASR and keyword spotting. PCBFA offers a new powerful understanding in acoustic-modeling of distant speech.     

A heterogeneous speech feature vectors generation approach with hybrid hmm classifiers

Automatic speech recognition (ASR) system plays a vital role in the human–machine interaction. ASR system faces the challenge of performance degradation due to inconsistency between training and testing phases. This occurs due to extraction and representation of erroneous, redundant feature vectors. This paper proposes three different combinations at speech feature vector generation phase and two hybrid classifiers at modeling phase. In feature extraction phase MFCC, RASTA-PLP, and PLP are combined in different ways. In modeling phase, the mean and variance are calculated to generate the inter and intra class feature vectors. These feature vectors are further adopted by optimization algorithm to generate refined feature vectors with traditional statistical technique. This approach uses GA?+?HMM and DE?+?HMM techniques to produce refine model parameters. The experiments are conducted on datasets of large vocabulary isolated Punjabi lexicons. The simulation result shows the performance improvement using MFCC and DE?+?HMM technique when compared with RASTA-PLP, PLP using hybrid HMM classifiers.     

改进的HMM和小波神经网络的抗噪语音识别

通过MFFC计算出的语音特征系数,由于语音信号的动态性,帧之间有重叠,噪声的影响,使特征系数不能完全反映出语音的信息。提出一种隐马尔可夫模型（HMM）和小波神经网络（WNN）混合模型的抗噪语音识别方法。该方法对MFCC特征系数利用小波神经网络进行训练,得到新的MFCC特征系数。实验结果表明,在噪声环境下,该混合模型比单纯HMM具有更强的噪声鲁棒性,明显改善了语音识别系统的性能。     

Speech-driven talking face using embedded confusable system for real time mobile multimedia

This paper presents a real-time speech-driven talking face system which provides low computational complexity and smoothly visual sense. A novel embedded confusable system is proposed to generate an efficient phoneme-viseme mapping table which is constructed by phoneme grouping using Houtgast similarity approach based on the results of viseme similarity estimation using histogram distance, according to the concept of viseme visually ambiguous. The generated mapping table can simplify the mapping problem and promote viseme classification accuracy. The implemented real time speech-driven talking face system includes: 1) speech signal processing, including SNR-aware speech enhancement for noise reduction and ICA-based feature set extractions for robust acoustic feature vectors; 2) recognition network processing, HMM and MCSVM are combined as a recognition network approach for phoneme recognition and viseme classification, which HMM is good at dealing with sequential inputs, while MCSVM shows superior performance in classifying with good generalization properties, especially for limited samples. The phoneme-viseme mapping table is used for MCSVM to classify the observation sequence of HMM results, which the viseme class is belong to; 3) visual processing, arranges lip shape image of visemes in time sequence, and presents more authenticity using a dynamic alpha blending with different alpha value settings. Presented by the experiments, the used speech signal processing with noise speech comparing with clean speech, could gain 1.1 % (16.7 % to 15.6 %) and 4.8 % (30.4 % to 35.2 %) accuracy rate improvements in PER and WER, respectively. For viseme classification, the error rate is decreased from 19.22 % to 9.37 %. Last, we simulated a GSM communication between mobile phone and PC for visual quality rating and speech driven feeling using mean opinion score. Therefore, our method reduces the number of visemes and lip shape images by confusable sets and enables real-time operation.     

多噪声环境下的层级语音识别模型

针对多噪声环境下的语音识别问题,提出了将环境噪声作为语音识别上下文考虑的层级语音识别模型。该模型由含噪语音分类模型和特定噪声环境下的声学模型两层组成,通过含噪语音分类模型降低训练数据与测试数据的差异,消除了特征空间研究对噪声稳定性的限制,并且克服了传统多类型训练在某些噪声环境下识别准确率低的弊端,又通过深度神经网络（DNN）进行声学模型建模,进一步增强声学模型分辨噪声的能力,从而提高模型空间语音识别的噪声鲁棒性。实验中将所提模型与多类型训练得到的基准模型进行对比,结果显示所提层级语音识别模型较该基准模型的词错率（WER）相对降低了20.3%,表明该层级语音识别模型有利于增强语音识别的噪声鲁棒性。