用于自动语音识别系统的切换语音功率谱估计算法

针对语音识别系统在噪声环境下不能保持很好鲁棒性的问题,提出了一种切换语音功率谱估计算法。该算法假设语音的幅度谱服从Chi分布,提出了一种改进的基于最小均方误差（MMSE）的语音功率谱估计算法。然后,结合语音存在的概率（SPP）,推导出改进的基于语音存在概率的MMSE估计器。接下来,将改进的MSME估计器与传统的维纳滤波器结合。在噪声干扰比较大时,使用改进的MMSE估计器来估计纯净语音的功率谱,当噪声干扰较小时,改用传统的维纳滤波器以减少计算量,最终得到用于识别系统的切换语音功率谱估计算法。实验结果表明,所提算法相比传统的瑞利分布下的MMSE估计器在各种噪声的情况下识别率平均提高在8个百分点左右,在去除噪声干扰、提高识别系统鲁棒性的同时,减小了语音识别系统的功耗。     

Bayesian on-line spectral change point detection: a soft computing approach for on-line ASR

Current automatic speech recognition (ASR) works in off-line mode and needs prior knowledge of the stationary or quasi-stationary test conditions for expected word recognition accuracy. These requirements limit the application of ASR for real-world applications where test conditions are highly non-stationary and are not known a priori. This paper presents an innovative frame dynamic rapid adaptation and noise compensation technique for tracking highly non-stationary noises and its application for on-line ASR. The proposed algorithm is based on a soft computing model using Bayesian on-line inference for spectral change point detection (BOSCPD) in unknown non-stationary noises. BOSCPD is tested with the MCRA noise tracking technique for on-line rapid environmental change learning in different non-stationary noise scenarios. The test results show that the proposed BOSCPD technique reduces the delay in spectral change point detection significantly compared to the baseline MCRA and its derivatives. The proposed BOSCPD soft computing model is tested for joint additive and channel distortions compensation (JAC)-based on-line ASR in unknown test conditions using non-stationary noisy speech samples from the Aurora 2 speech database. The simulation results for the on-line AR show significant improvement in recognition accuracy compared to the baseline Aurora 2 distributed speech recognition (DSR) in batch-mode.     

Evolutionary approach for integration of multiple pronunciation patterns for enhancement of dysarthric speech recognition

Dysarthria is a motor speech disorder caused by neurological injury of the motor component of the motor-speech system. Because it affects respiration, phonation, and articulation, it leads to different types of impairments in intelligibility, audibility, and efficiency of vocal communication. Speech Assistive Technology (SAT) has been developed with different approaches for dysarthric speech and in this paper we focus on the approach that is based on modeling of pronunciation patterns. We present an approach that integrates multiple pronunciation patterns for enhancement of dysarthric speech recognition. This integration is performed by weighting the responses of an Automatic Speech Recognition (ASR) system when different language model restrictions are set. The weight for each response is estimated by a Genetic Algorithm (GA) that also optimizes the structure of the implementation technique (Metamodels) which is based on discrete Hidden Markov Models (HMMs). The GA makes use of dynamic uniform mutation/crossover to further diversify the candidate sets of weights and structures to improve the performance of the Metamodels. To test the approach with a larger vocabulary than in previous works, we orthographically and phonetically labeled extended acoustic resources from the Nemours database of dysarthric speech. ASR tests on these resources with the proposed approach showed recognition accuracies over those obtained with standard Metamodels and a well used speaker adaptation technique. These results were statistically significant.     

实用环境语音识别鲁棒性技术研究与展望

语音识别系统在实用环境中的鲁棒性是语音识别技术实用化的关键问题。鲁棒性研究的核心问题是如何解决实用环境语音特征和模型与干净环境语音识别系统的失配问题,这涉及到噪声补偿、信道适应、说话人自适应等关键技术。文章综述了语音识别鲁棒性技术研究的主要方法、原理及研究现状,分析了实用环境语音识别中声学模型和语言模型的适应技术,并展望了近期语音识别实用化技术发展的研究方向。     

Performance analysis of ASR system in hybrid DNN-HMM framework using a PWL euclidean activation function

Automatic Speech Recognition (ASR) is the process of mapping an acoustic speech signal into a human readable text format. Traditional systems exploit the Acoustic Component of ASR using the Gaussian Mixture Model- Hidden Markov Model (GMM-HMM) approach.Deep NeuralNetwork (DNN) opens up new possibilities to overcome the shortcomings of conventional statistical algorithms. Recent studies modeled the acoustic component of ASR system using DNN in the so called hybrid DNN-HMM approach. In the context of activation functions used to model the non-linearity in DNN, Rectified Linear Units (ReLU) and maxout units are mostly used in ASR systems. This paper concentrates on the acoustic component of a hybrid DNN-HMM system by proposing an efficient activation function for the DNN network. Inspired by previous works, euclidean norm activation function is proposed to model the non-linearity of the DNN network. Such non-linearity is shown to belong to the family of Piecewise Linear (PWL) functions having distinct features. These functions can capture deep hierarchical features of the pattern. The relevance of the proposal is examined in depth both theoretically and experimentally. The performance of the developed ASR system is evaluated in terms of Phone Error Rate (PER) using TIMIT database. Experimental results achieve a relative increase in performance by using the proposed function over conventional activation functions.     

Multi-band automatic speech recognition

This paper presents a new architecture for automatic speech recognition systems which is characterized by the division of the spectral domain of the speech signal into several independent frequency bands. This model is based on the psycho-acoustic work of Fletcher (1953) who proposed a similar principle for the human auditory system. Jont B. Allen published a paper in 1994 in which he summarized the work of Fletcher and also proposed to adapt the multi-band paradigm to automatic speech recognition (ASR) (Allen, 1994). Many researchers have then studied this principle and built such ASR systems. The goal of this paper is to analyse some of the most important issues in the design of a multi-band ASR system in order to determine which architecture it should have in which environment. Two other major problems are then considered: how to train multi-band systems and how to use them for continuous ASR.     

A Robust Speech Recognition System for Communication Robots in Noisy Environments

The application range of communication robots could be widely expanded by the use of automatic speech recognition (ASR) systems with improved robustness for noise and for speakers of different ages. In past researches, several modules have been proposed and evaluated for improving the robustness of ASR systems in noisy environments. However, this performance might be degraded when applied to robots, due to problems caused by distant speech and the robot's own noise. In this paper, we implemented the individual modules in a humanoid robot, and evaluated the ASR performance in a real-world noisy environment for adults' and children's speech. The performance of each module was verified by adding different levels of real environment noise recorded in a cafeteria. Experimental results indicated that our ASR system could achieve over 80% word accuracy in 70-dBA noise. Further evaluation of adult speech recorded in a real noisy environment resulted in 73% word accuracy.     

Two-stage model-based feature compensation for robust speech recognition

This paper presents a combination approach to robust speech recognition by using two-stage model-based feature compensation. Gaussian mixture model (GMM)-based and hidden Markov model (HMM)-based compensation approaches are combined together and conducted sequentially in the multiple-decoding recognition system. The clean speech is firstly modeled as a GMM in the initial pass, and then modeled as a HMM generated from the initial pass in the following passes, respectively. The environment parameter estimation on these two modeling strategies are formulated both under maximum a posteriori (MAP) criterion. Experimental result shows that a significant improvement is achieved compared to European Telecommunications Standards Institute (ETSI) advanced compensation approach, GMM-based feature compensation approach, HMM-based feature compensation approach, and acoustic model compensation approach.     

A new framework for robust speech recognition in complex channel environments

Channel distortion is one of the major factors which degrade the performances of automatic speech recognition (ASR) systems. Current compensation methods are generally based on the assumption that the channel distortion is a constant or slowly varying bias in an utterance or globally. However, this assumption is not sustained in a more complex circumstance, when the speech records being recognized are from many different unknown channels and have parts of the spectrum completely removed (e.g. band-limited speech). On the one hand, different channels may cause different distortions; on the other, the distortion caused by a given channel varies over the speech frames when parts of the speech spectrum are removed completely. As a result, the performance of the current methods is limited in complex environments. To solve this problem, we propose a unified framework in which the channel distortion is first divided into two subproblems, namely, spectrum missing and magnitude changing. Next, the two types of distortions are compensated with different techniques in two steps. In the first step, the speech bandwidth is detected for each utterance and the acoustic models are synthesized with clean models to compensate for spectrum missing. In the second step, the constant term of the distortion is estimated via the expectation-maximization (EM) algorithm and subtracted from the means of the synthesized model to further compensate for magnitude changing. Several databases are chosen to evaluate the proposed framework. The speech in these databases is recorded in different channels, including various microphones and band-limited channels. Moreover, to simulate more types of spectrum missing, various low-pass and band-pass filters are used to process the speech from the chosen databases. Although these databases and their filtered versions make the channel conditions more challenging for recognition, experimental results show that the proposed framework can substantially improve the performance of ASR systems in complex channel environments.     

Past review,current progress,and challenges ahead on the cocktail party problem

The cocktail party problem, i.e., tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously, is one of the critical problems yet to be solved to enable the wide application of automatic speech recognition (ASR) systems. In this overview paper, we review the techniques proposed in the last two decades in attacking this problem. We focus our discussions on the speech separation problem given its central role in the cocktail party environment, and describe the conventional single-channel techniques such as computational auditory scene analysis (CASA), non-negative matrix factorization (NMF) and generative models, the conventional multi-channel techniques such as beamforming and multi-channel blind source separation, and the newly developed deep learning-based techniques, such as deep clustering (DPCL), the deep attractor network (DANet), and permutation invariant training (PIT). We also present techniques developed to improve ASR accuracy and speaker identification in the cocktail party environment. We argue effectively exploiting information in the microphone array, the acoustic training set, and the language itself using a more powerful model. Better optimization objective and techniques will be the approach to solving the cocktail party problem.     

The use of phase in complex spectrum subtraction for robust speech recognition

In this paper we propose a new method for utilising phase information by complementing it with traditional magnitude-only spectral subtraction speech enhancement through complex spectrum subtraction (CSS). The proposed approach has the following advantages over traditional magnitude-only spectral subtraction: (a) it introduces complementary information to the enhancement algorithm; (b) it reduces the total number of algorithmic parameters; and (c) is designed for improving clean speech magnitude spectra and is therefore suitable for both automatic speech recognition (ASR) and speech perception applications. Oracle-based ASR experiments verify this approach, showing an average of 20% relative word accuracy improvements when accurate estimates of the phase spectrum are available. Based on sinusoidal analysis and assuming stationarity between observations (which is shown to be better approximated as the frame rate is increased), this paper also proposes a novel method for acquiring the phase information called Phase Estimation via Delay Projection (PEDEP). Further oracle ASR experiments validate the potential for the proposed PEDEP technique in ideal conditions. Realistic implementation of CSS with PEDEP shows performance comparable to state of the art spectral subtraction techniques in a range of 15–20 dB signal-to-noise ratio environments. These results clearly demonstrate the potential for using phase spectra in spectral subtractive enhancement applications, and at the same time highlight the need for deriving more accurate phase estimates in a wider range of noise conditions.     

Non-linear feature extraction for robust speech recognition in stationary and non-stationary noise

An analysis-based non-linear feature extraction approach is proposed, inspired by a model of how speech amplitude spectra are affected by additive noise. Acoustic features are extracted based on the noise-robust parts of speech spectra without losing discriminative information. Two non-linear processing methods, harmonic demodulation and spectral peak-to-valley ratio locking, are designed to minimize mismatch between clean and noisy speech features. A previously studied method, peak isolation [IEEE Transactions on Speech and Audio Processing 5 (1997) 451], is also discussed with this model. These methods do not require noise estimation and are effective in dealing with both stationary and non-stationary noise. In the presence of additive noise, ASR experiments show that using these techniques in the computation of MFCCs improves recognition performance greatly. For the TI46 isolated digits database, the average recognition rate across several SNRs is improved from 60% (using unmodified MFCCs) to 95% (using the proposed techniques) with additive speech-shaped noise. For the Aurora 2 connected digit-string database, the average recognition rate across different noise types, including non-stationary noise background, and SNRs improves from 58% to 80%.     

An efficient speech recognition system in adverse conditions using the nonparametric regression

General Regression Neural Networks (GRNN) have been applied to phoneme identification and isolated word recognition in clean speech. In this paper, the authors extended this approach to Arabic spoken word recognition in adverse conditions. In fact, noise robustness is one of the most challenging problems in Automatic Speech Recognition (ASR) and most of the existing recognition methods, which have shown to be highly efficient under noise-free conditions, fail drastically in noisy environments. The proposed system was tested for Arabic digit recognition at different Signal-to-Noise Ratio (SNR) levels and under four noisy conditions: multispeakers babble background, car production hall (factory), military vehicle (leopard tank) and fighter jet cockpit (buccaneer) issued from NOISEX-92 database. The proposed scheme was successfully compared to the similar recognizers based on the Multilayer Perceptrons (MLP), the Elman Recurrent Neural Network (RNN) and the discrete Hidden Markov Model (HMM). The experimental results showed that the use of nonparametric regression with an appropriate smoothing factor (spread) improved the generalization power of the neural network and the global performance of the speech recognizer in noisy environments.     

基于顺序统计滤波的实时语音端点检测算法

针对嵌入式语音识别系统,提出了一种高效的实时语音端点检测算法. 算法以子带频谱熵为语音/噪声的区分特征, 首先将每帧语音的频谱划分成若干个子带, 计算出每个子带的频谱熵, 然后把相继若干帧的子带频谱熵经过一组顺序统计滤波器获得每帧的频谱熵, 根据频谱熵的值对输入的语音进行分类. 实验结果表明, 该算法能够有效地区分语音和噪声, 可以显著地提高语音识别系统的性能. 在不同的噪声环境和信噪比条件下具有鲁棒性. 此外, 本文提出的算法计算代价小, 简单易实现, 适合实时嵌入式语音识别系统的应用.     

Unsupervised noise reduction scheme for voice-based information retrieval in mobile environments

This study proposes an unsupervised noise reduction scheme that improves the performance of voice-based information retrieval tasks in mobile environments. Various types of noises could interfere with speech processing tasks, and noise reduction has become an essential technique in this field. In particular, noise reduction needs to be carefully processed in mobile environments based on the speech coding system and the client-server architecture. In this study, we propose an effective noise reduction scheme that employs the adaptive comb filtering technique. A way of directly using several codec parameters during the filtering process is also investigated. In particular, we modify the conventional comb filter using line spectral pair parameters. To verify the efficiency of the proposed noise reduction approach, we conducted speech recognition experiments using the Aurora2 database. Our approach provided superior recognition performance under various noise conditions compared to the conventional techniques.     

Global scheduling for flexible transactions in heterogeneousdistributed database systems

A heterogeneous distributed database environment integrates a set of autonomous database systems to provide global database functions. A flexible transaction approach has been proposed for the heterogeneous distributed database environments. In such an environment, flexible transactions can increase the failure resilience of global transactions by allowing alternate (but in some sense equivalent) executions to be attempted when a local database system fails or some subtransactions of the global transaction abort. We study the impact of compensation, retry, and switching to alternative executions on global concurrency control for the execution of flexible transactions. We propose a new concurrency control criterion for the execution of flexible and local transactions, termed F-serializability, in the error-prone heterogeneous distributed database environments. We then present a scheduling protocol that ensures F-serializability on global schedules. We also demonstrate that this scheduler avoids unnecessary aborts and compensation     

Mixed environment compensation based on maximum a posteriori estimation for robust speech recognition

Noise robustness is a fundamental problem for speech recognition system in the real environments. The paper presents mixed environment compensation technique in which feature compensation algorithm and acoustic model compensation algorithm is combined together. The target is to obtain the fine compensated static acoustic model and the dynamic compensated speech. Therefore, the modified speech sequence can well match the modified acoustic model. The experimental results show that significant performance improvement has been observed.     

Robust regression fusion of GMM-UBM and GMM-SVM normalized scores using G729 bit-stream for speaker recognition over IP

A novel approach, based on robust regression with normalized score fusion (namely Normalized Scores following Robust Regression Fusion: NSRRF), is proposed for enhancement of speaker recognition over IP networks, which can be used both in Network Speaker Recognition (NSR) and Distributed Speaker Recognition (DSR) systems. In this framework, it is basically assumed that the speech must be encoded by G729 coder in client side, and then, transmitted at a server side, where the ASR systems are located. The Universal Background Gaussian Mixture Model (GMM-UBM) and Gaussian Supervector (GMM-SVM) with normalized scores are used for speaker recognition. In this work, Mel Frequency Cepstral Coefficient (MFCC) and Linear Prediction Cepstral Coefficient (LPCC), both of these features are derived from Line Spectral Pairs (LSP) extracted from G729 bit-stream over IP, constitute the features vectors. Experimental results, conducted with the LIA SpkDet system based on the ALIZE platform3 using ARADIGITS database, have shown in first that the proposed method using features extracted directly from G729 bit-stream reduces significantly the error rate and outperforms the baseline system in ASR over IP based on the resynthesized (reconstructed) speech obtained from the G729 decoder. In addition, the obtained results show that the proposed approach, based on scores normalization following robust regression fusion technique, achieves the best result and outperform the conventional ASR over IP network.     

Exploring the Use of Speech Features and Their Corresponding Distribution Characteristics for Robust Speech Recognition

The performance of current automatic speech recognition (ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources. Several methods have been proposed to improve ASR robustness over the last few decades. The related literature can be generally classified into two categories according to whether the methods are directly based on the feature domain or consider some specific statistical feature characteristics. In this paper, we present a polynomial regression approach that has the merit of directly characterizing the relationship between speech features and their corresponding distribution characteristics to compensate for noise interference. The proposed approach and a variant were thoroughly investigated and compared with a few existing noise robustness approaches. All experiments were conducted using the Aurora-2 database and task. The results show that our approaches achieve considerable word error rate reductions over the baseline system and are comparable to most of the conventional robustness approaches discussed in this paper.