Auditory analysis for speech recognition based on physiological models

A comparative study is presented of three noise-compensation schemes, namely spectral subtraction, Wiener filters, and noise adaptation, for hidden-Markov-model-based speech recognition in adverse environments. The noise-compensation methods are evaluated on a spoken-digit database, in the presence of car noise and helicopter noise at different signal-to-noise ratios. Experimental results demonstrate that the noise-compensation methods achieve a substantial improvement in recognition accuracy across a wide range of signal-to-noise ratios. At a signal-to-noise ratio of -6 dB the recognition accuracy is improved from 11% to 83%. The use of cepstral-time matrices as an improved speech representation is also considered, and their combination with the noise-compensation methods is shown. Experiments show that the cepstral-time matrix is a more robust feature than a vector of identical size, composed of a combination of cepstral and differential cepstral features.

Combined speech enhancement and auditory modelling for robust distributed speech recognition

In this paper, we propose a joint optimal method for automatic speech recognition (ASR) and ideal binary mask (IBM) estimation in transformed into the cepstral domain through a newly derived generalized expectation maximization algorithm. First, cepstral domain missing feature marginalization is established using a linear transformation, after tying the mean and variance of non-existing cepstral coefficients. Second, IBM estimation is formulated using a generalized expectation maximization algorithm directly to optimize the ASR performance. Experimental results show that even in highly non-stationary mismatch condition (dance music as background noise), the proposed method achieves much higher absolute ASR accuracy improvement ranging from 14.69% at 0 dB SNR to 40.10% at 15 dB SNR compared with the conventional noise suppression method.

In this contribution, a two-channel acoustic front-end for robust automatic speech recognition (ASR) in adverse acoustic environments is analyzed. The source signal extraction scheme combines a blocking matrix based on semi-blind source separation, which provides a continuously updated reference of all undesired components separated from the desired signal and its reflections, and a single-channel Wiener postfilter. The postfilter is directly derived from the obtained noise and interference reference signal and hence, generalizes well-known postfilter realizations. The proposed front-end and its integration into an ASR system are analyzed and evaluated with respect to keyword accuracy under reverberant conditions with unpredictable and nonstationary interferences, and for different target source distances. Evaluating a simplified front-end based on free-field assumptions, an ideal front-end, where knowledge about the true undesired components is assumed, and comparing the proposed scheme with the competitive approach of solely using multistyle training, demonstrates the importance of an adequate signal preprocessing for robust distant speech recognition.

We propose a novel exemplar-based feature enhancement method for automatic speech recognition which uses coupled dictionaries: an input dictionary containing atoms sampled in the modulation (envelope) spectrogram domain and an output dictionary with atoms in the Mel or full-resolution frequency domain. The input modulation representation is chosen for its separation properties of speech and noise and for its relation with human auditory processing. The output representation is one which can be processed by the ASR back-end. The proposed method was investigated on the AURORA-2 and AURORA-4 databases and improved word error rates (WER) were obtained when compared to the system which uses Mel features in the input exemplars. The paper also proposes a hybrid system which combines the baseline and the proposed algorithm on the AURORA-2 database which in turn also yielded improvement over both the algorithms.

Automatic Speech Recognition has been an active topic of research for the past four decades. The main objective of the automatic speech recognition task is to convert a speech segment into an interpretable text message without the need of human intervention. Many different algorithms and schemes based on different mathematical paradigms have been proposed in an attempt to improve recognition rates. Cepstral coefficients play an important part in speech theory and in automatic speech recognition in particular due to their ability to compactly represent relevant information that is contained in a short time sample of a continuous speech signal. The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and improved Mel-Frequency Cepstrum Coefficients (MFCC) using RASTA filters. Thus, in this study, we try to improve the MFCC algorithms to achieve much accuracy reducing the error rates in Automatic Speech Recognition. First, we remove signal correlation through normalization, then we use RASTA filter to filtering the cepstral coefficients. Finally, we reduce dimension of the cepstral coefficients by the variances of cepstral coefficients in different dimension and obtain our features. By using various classifiers, we try to simulate the speech feature extraction at much optimal and least error rate providing robust method for Automatic Speech Recognition (ASRs).KeywordsAutomatic Speech RecognitionMel frequency Cepstrum Coefficients (MFCC’s)ERB Gammatone FilteringHidden Markov Model

Human computer interaction (HCI) is very crucial in our day-to-day activity. Speech is one of the essential and intuitive ways to interact with machines such as Smartphone, which has multiple sensors as microphone, camera, etc. An efficient performance speech recognition system improves interaction between man and machines by making latter more receptive to user needs. Such system has Automatic speech recognition (ASR) engine, which is facing a unique challenge of accuracy in recognition rate. By integrating acoustic signal feature vectors with the visual features, a more robust audiovisual speech recognition engine (AVSR) could be developed for real environmental scenarios. This paper presents past research and development in the field of ASR and AVSR technologies. It describes key technological perspective and admiration of the fundamental progress in ASR and AVSR. The objective of this review is to summarize and compare some of the well-known methods experimented by previous researchers, and to conclude with direction on future research proficiency in HCI system using ASR and AVSR engine.

This paper describes the structure and performance of a new signal processing scheme, motivated by the physiology of the peripheral auditory system, that improves speech recognition accuracy in the presence of broadband noise. An important attribute of the peripheral processing is a novel mechanism to represent the cycle-by-cycle synchrony in the response of low-frequency auditory-nerve fibers, in addition to the more conventional processing based on mean rate of response. It is shown that the use of the physiologically-motivated peripheral processing improves recognition accuracy in the presence of both broadband and transient noise, and that the use of the synchrony mechanism provides further improvement beyond that which is provided by the mean rate mechanism. Index Terms: auditory modeling, robust speech recognition, auditory snchrony.

The article discusses the system of dialogue control manipulation robots. The analysis of the basic methods of automatic speech recognition, speech understanding, dialogue management, voice response synthesis in dialogue systems has been carried out. Three types of dialogue management are considered as "system initiative", "user initiative" and "combined initiative". A system of object-oriented dialog control of a robot based on the theory of finite state machines with using a deep neural network is proposed. The main difference of the proposed system lies in the separate implementation of the dialogue process and robot’s actions, which is close to the pace of natural dialogue control. This method of constructing a dialogue control robot allows system to automatically correct the result of speech recognition, robot’s actions based on tasks. The necessity of correcting the result of speech recognition and robot’s actions may be caused by the users’ accent, working environment noise or incorrect voice commands. The process of correcting speech recognition results and robot’s actions consists of three stages, respectively, in a special mode and a general mode. The special mode allows users to directly control the manipulator by voice commands. The general mode extends the capabilities of users, allowing them to get additional information in real time. At the first stage, continuous speech recognition is built by using a deep neural network, taking into account the accents and speech speeds of various users. Continuous speech recognition is a real-time voice to text conversion. At the second stage, the correction of the speech recognition result by managing the dialogue based on the theory of finite automata. At the third stage, the actions of the robot are corrected depending on the operating state of the robot and the dialogue management process. In order to realize a natural dialogue between users and robots, the problem is solved in creating a small database of possible dialogues and using various training data. In the experiments, the dialogue system is used to control the KUKA manipulator (KRC4 control) to put the desired block in the specified location, implemented in the Python environment using the RoboDK software. The processes and results of experiments confirming the operability of the interactive robot control system are given. A fairly high accuracy (92 %) and an automatic speech recognition rate close to the rate of natural speech were obtained.

This paper introduces a robust human-robot interface (HRI) system using a speech recognition and a user localization. For a robust speech recognition indoors under unknown noises and acoustic reverberations, a blind source separation (BSS) algorithm is implemented by a block-wise processing and developed using digital signal processing board to guarantee real-time operation. And a reverberation-robust sound source localization algorithm using separated signals is proposed. Although the BSS method cannot completely preserve the room acoustic information, the proposed localization algorithm overcomes this problem using target channel selection and target-emphasized enhancement methods. The developed algorithms are integrated into the commercial robot system to provide overall voice-enabled HRI. A series of tests are conducted to evaluate the performance of the BSS-based speech recognition and user localization method, and the results show a remarkable performance even under severe non-stationary noise conditions.

A new robust speech recognition method combining adaptive filtering based on a variable-step LMS algorithm and perceptual linear prediction cepstral estimation is proposed. The variable-step LMS algorithm is realized by optimizing the non-linear relation between step size and mean square error. The perceptual linear prediction cepstral extracted from the enhanced speech is used for generating the speech feature parameters. The experimental results show that the performance of this robust speech recognition method is better than that of the speech recognition method based on Sigmind function under lower signal-to-noise ratio.

Speech and language recognition is one of the most important requirements in daily life. Most people in the world use English as the highly preferable language for easy communication. As a result, English is the highest speaking language in the World. Speech is the easiest way of medium for communication. Hunan-human, human-machine, and machine-machine communication are very much dependent on extensive and robust speech recognition. There are various important features of speech. The accent is one of them. It is a unique feature of speech that varies according to region and dialect. Speech scientists are continuously working on the improvement of speech recognition techniques and performance. Various challenges are being faced day by day and new areas of research are opened to overcome those difficulties. Variation of accent is one of the biggest challenges in improving speech recognition performances. In this work, the native and non-native English accent has been considered. The classification task has been performed on the English speech of non-native speakers and native American English speakers. Three sets of male and female speaker data both for native and non-native speech have been collected from the Voice Cloning Toolkit corpus of the Centre for Speech Technology Research. Mel Frequency Cepstral Coefficient features were extracted from the audio signals and given as input parameters to the supervised machine learning models. Evaluation metrics have been generated, multiple results were produced and compared.

Systems and methods of automatic speech recognition are described. In one aspect, an automatic speech recognition system includes a speech recognition dictionary and a speech recognizer. The speech recognition dictionary includes a plurality of meaning tokens each associated with one or more pronunciations of one or more vocabulary words and signifying a single meaning. The speech recognizer is configured to convert spoken input into a sequence of meaning tokens contained in the speech recognition dictionary and corresponding to a sequence of vocabulary words most likely to have been spoken by a user.

This paper compares several different approaches to robust speech recognition. We review CMU's ongoing research in the use of acoustical pre-processing to achieve robust speech recognition, and we present the results of the first evaluation of preprocessing in the context of the DARPA standard ATIS domain for spoken language systems. We also describe and compare the effectiveness of three complementary methods of signal processing for robust speech recognition: acoustical pre-processing, microphone array processing, and the use of physiologically-motivated models of peripheral signal processing. Recognition error rates are presented using these three approaches in isolation and in combination with each other for the speaker-independent continuous alphanumeric census speech recognition task.

A new trial on speech recognition from graphical point of view is introduced. Isolated spoken-letters and color-names words are considered. After recording, the speech signal is processed as an image by Power Spectrum Estimation. For feature extraction, classification and hence recognition, the algorithm of minimal eigenvalues of Toeplitz matrices together with other methods of speech processing and recognition are used. A number of examples on applications and comparisons are presented in the work. The efficiency of the method is very high in the case of the six Polish vowels and English color- names, and the results are encouraging to extend the algorithm to cover more word classes.