Speech Recognition in Indian Languages—A Survey

Arabic language can be used by native and non- native speakers; due to Arabic is the language of the holy book of Muslims. In this paper, Arabic phoneme recognition system is proposed based on Malay speakers. This system consists of three main stages. The first stage is noise reduction and it aims to enhance the phoneme signals by excluding the unvoiced signals and keep only the voiced signal. Wiener filter is adapted to accomplish this task. The second stage is based on Mel-Frequency Cepstral Coefficients method to extract a vector of features to represent each phoneme signal. Eventually, pattern recognition neural network is designed as recognizer. The proposed system produces sufficient outcomes with 20 hidden neurons. Keywords—Arabic; Malay; pattern recognition; Wiener; Mel- Frequency Cepstral Coefficients I. INTRODUCTION It is well known the wide range of Artificial Neural Networks (ANN) application in speech recognition, financial, telecommunications, electronics and medical. As for speech recognition system for Arabic language, (1) & (2) have addressed the implementation of recognition system based on Arabic native speakers. In general, neural networks (NNs) for phonemes recognition are divided into several categories for instance Very Large Scale Integration (VLSI) NN that can be divided into digital and analogue with digital NNs being more compatible with feed-forward neural networks while analogue NNs are found to be more successful with recurrent NNs (3). From Automatic Speech System (ASR) point of view, NNs for recognition purpose can be divided into two main category namely conventional neural networks such as Multilayer Perceptron (MLP) and Radial Basis Function (RBF) and secondly Recurrent Neural Networks (RNN). The first category of NNs was implemented as pattern classifiers and proven capable for recognition purpose but not at par as compared to Hidden Markov Model (HMM) performance (2). At present, HMM has been proven to be the most successful approach in ASR research area until recently the possibility to hybrid both HMM and ANN (4) & (5). In general, speech recognition systems consist of three stages specifically noise reduction, feature extraction and classification. The aim of noise reduction is to reduce the level of noise in the speech signal and make it more compatible for the ASR. As for feature extraction, this process attempted to extract the most relevant features related to the phoneme signal. The extracted features acted as input features of the NN during training and testing. Finally, the purpose of classification process is to arrange the most similar and related features in categories based on the training phase. Basically, networks training database are being observed and updated during training session of the networks, involving the weights and biases arguments. Networks with lower errors can be considered as better networks (6). Hence, this paper deems to explore further the ability of NN for Arabic phonemes recognition spoken by Malay individuals. This paper is arranged as following; related work is discussed in section II followed by section III that discussed method to be implemented based on Wiener filter, Mel-Frequency Cepstral Coefficients and pattern recognition neural network. Section IV elaborated experimental analysis and results and followed by Conclusion section.

Combined speech enhancement and auditory modelling for robust distributed speech recognition

In this paper we investigated Artificial Neural Networks (ANN) based Automatic Speech Recognition (ASR) by using limited Arabic vocabulary corpora. These limited Arabic vocabulary subsets are digits and vowels carried by specific carrier words. In addition to this, Hidden Markov Model (HMM) based ASR systems are designed and compared to two ANN based systems, namely Multilayer Perceptron (MLP) and recurrent architectures, by using the same corpora. All systems are isolated word speech recognizers. The ANN based recognition system achieved 99.5% correct digit recognition. On the other hand, the HMM based recognition system achieved 98.1% correct digit recognition. With vowels carrier words, the MLP and recurrent ANN based recognition systems achieved 92.13% and 98.06, respectively, correct vowel recognition; but the HMM based recognition system achieved 91.6% correct vowel recognition.

Recently, deep neural network (DNN) with hidden Markov model (HMM) has turned out to be a superior sequence learning framework, based on which significant improvements were achieved in many application tasks, such as automatic speech recognition (ASR). However, the training of DNN-HMM requires the pre-segmented training data, which can be generated using Gaussian Mixture Model (GMM) in ASR tasks. Thus, questions are raised by many researchers: can we train the DNN-HMM without GMM seeding, and what does it suggest if the answer is yes? In this research, we come up with the ‘yes’ answer by presenting forward-backward learning algorithm for DNN-HMM framework. Besides, a training procedure is proposed, in which, the training for context independent (CI) DNN-HMM is treated as the pre-training for context dependent (CD) DNN-HMM. To evaluate the contribution of this work, experiments on ASR task with the benchmark corpus TIMIT are performed, and the results demonstrate the effectiveness of this research.

The Relative Distance Vector Neural Network (RDVNN) Model: A Hybrid Approach to Speech Recognition

In this paper we investigated Artificial Neural Networks (ANN) based Automatic Speech Recognition (ASR) by using limited Arabic vocabulary corpora. These limited Arabic vocabulary subsets are digits and vowels carried by specific carrier words. In addition to this, Hidden Markov Model (HMM) based ASR systems are designed and compared to ANN based systems by using the same corpora. All systems are isolated word speech recognizers. The ANN based recognition system achieved 99.5% correct digit recognition. On the other hand, the HMM based recognition system achieved 98.1% correct digit recognition. With vowels carrier words, the ANN based recognition system achieved 92.13% correct vowel recognition; but the HMM based recognition system achieved 91.6% correct vowel recognition.

This paper introduces a unified framework for online adaptation of hidden Markov models (HMM) parameters to real-life conditions. Hence, it aims at improving the robustness of speech recognition systems. In addition, it describes some techniques developed to control the convergence of adaptation in unsupervised modes. Classically, two approaches have been used to adapt HMM parameters to new conditions, that is, Bayesian adaptation and spectral transformation-generally using linear regression. This paper lays out a unifying framework where both Bayesian adaptation and spectral transformation adaptation are seen as particular cases. In this sense, the framework attributes one transformation to each Gaussian distribution and partitions the latter automatically with respect to the adaptation data. Thus, the transformations of each class would share the same parameter vector. Consequently, the global transformation gets a data-driven freedom degree. The parameters of the global transformation are determined according to the maximum a posteriori (MAP) criterion using the original HMM a priori distributions. The general adaptation algorithm has been implemented within the CNET speech recognition system and the whole system evaluated on several field-telephone databases. The new adaptation method provides us with a systematic convergence in an online unsupervised mode of the speech recognition system toward a system enrolled with field data in a supervised mode.

This paper proposes and evaluates a new statistical discrimination measure for hidden Markov models (HMMs) extending the notion of divergence, a measure of average discrimination information originally defined for two probability density functions. Similar distance measures have been proposed for the case of HMMs, but those have focused primarily on the stationary behavior of the models. However, in speech recognition applications, the transient aspects of the models have a principal role in the discrimination process and, consequently, capturing this information is crucial in the formulation of any discrimination indicator. This paper proposes the notion of average divergence distance (ADD) as a statistical discrimination measure between two HMMs, considering the transient behavior of these models. This paper provides an analytical formulation of the proposed discrimination measure, a justification of its definition based on the Viterbi decoding approach, and a formal proof that this quantity is well defined for a left-to-right HMM topology with a final nonemitting state, a standard model for basic acoustic units in automatic speech recognition (ASR) systems. Using experiments based on this discrimination measure, it is shown that ADD provides a coherent way to evaluate the discrimination dissimilarity between acoustic models.

A model-based framework of classification error rate estimation is proposed for speech and speaker recognition. It aims at predicting the run-time performance of a hidden Markov model (HMM) based recognition system for a given task vocabulary and grammar without the need of running recognition experiments using a separate set of testing samples. This is highly desirable both in theory and in practice. However, the error rate expression in HMM-based speech recognition systems has no closed form solution due to the complexity of the multi-class comparison process and the need for dynamic time warping to handle various speech patterns. To alleviate the difficulty, we propose a one-dimensional model-based misclassification measure to evaluate the distance between a particular model of interest and a combination of many of its competing models. The error rate for a class characterized by the HMM is then the value of a smoothed zero-one error function given the misclassification measure. The overall error rate of the task vocabulary could then be computed as a function of all the available class error rates. The key here is to evaluate the misclassification measure in terms of the parameters of environmental-matched models without running recognition experiments, where the models are adapted by very limited data that could be just the testing utterance itself. In this paper, we show how the misclassification measure could be approximated by first computing the distance between two mixture Gaussian densities, then between two HMMs with mixture Gaussian state observation densities and finally between two sequences of HMMs. The misclassification measure is then converted into classification error rate. When comparing the error rate obtained in running actual experiments and that of the new framework, the proposed algorithm accurately estimates the classification error rate for many types of speech and speaker recognition problems. Based on the same framework, it is also demonstrated that the error rate of a recognition system in a noisy environment could also be predicted.

The fundamentals of speech recognition are reviewed. The dimensions of the speech recognition task, speech feature analysis, pattern classification using hidden Markov models, language processing, and the current accuracy of speech recognition systems are discussed. The applications of speech recognition in telecommunications, voice dictation, speech understanding for data retrieval, and consumer products are examined. >

Speech recognition systems aim to make human-machine communication quickly and easily. In recent years, various researches and studies have been carried out to develop speech recognition systems. Examples of these studies are speech recognition, speaker recognition and speaker verification. In this study, speech recognition systems were investigated, methods used in the literature were investigated and a Turkish speech recognition application was developed. The application consists of speech coding and speech recognition. Firstly 20 Turkish words which are frequently used on the computer were determined. There are 20 records from each word. A total of 400 words were recorded on the computer with a microphone. In the speech coding section of the application, these words recorded on the computer are encoded by the Linear Pre-estimation Coding (LPC) method and the LPC parameters for each word are obtained. In the speech recognition section of the application, the Support Vector Machines (SVM) method is used. Two types of SVM classifiers are designed. These are the Soft Margin SVM (SM-SVM) classifier and the Least Square SVM (LS-SVM) classifier. Classification consists of training and testing stages. Of the 400 coded words, 200 were used for the training phase and 200 were used for the testing phase. As a result, 91% accurate recognition success for the SM-SVM classifier; 71% correct recognition of the LS-SVM classifier has been achieved.

Voice Controlled MAV (Micro Air Vehicle) is an attractive alternative to flying the MAVs without a joystick/ mouse clicks. This being a command and control application calls for accurate and fast Speech Recognition. The paper proposes a feed forward neural network based speech recognition for voice controlled MAV application, which achieves better accuracy and faster recognition compared Viterbi algorithm which operates on statistical data. ANN (Artificial Neural Network) could achieve word accuracy of 93% against 85% as achieved by HMM (Hidden Markov Model). ANN achieved about 25% faster recognition compared to HMM.

Feature normalization based on non-extensive statistics for speech recognition

Growth in Digital signal processing technology gives positives influences in human life. One of the branch of science that gives significant influence is digital speech processing. It can be expand into some applications that make human life easier. Digital speech processing is appropriate to speech recognition. Speech recognition is used to arrange the movement of remote control car. The remote control car will move according to our speech. This final project is closely related with speech recognition. The LPC (Linear Predictive Coding) method will extract the speech signal features and HMM (Hidden Markov Model) to modelling the speech signal are used. It is done by comparing model from extracting feature that is available in HMM modelling. Models will be used in the speech recognition process, if the models have highest level of conformity. The experiment has been done in two conditions, i.e ideal condition in room wih low noise level and unideal condition in room with noise. Result of the experiment from the whole sistem performace at ideal condition is 97,71% for people that have been inputed in the database, and 95,42% for people which have not been inputed in the database. Result of the experiment of database say it regulary is 97,14%. Result of the experiment of database at unideal condition is 54,28% for inputing word at noisy area, result of the experiment of database for inputing word at high frequency from siren voice is 98,57% and result of the experiment of inputing similar word with database is 97,6% Keywords: speech recognition, LPC, HMM

This paper proposes a new model for a noise-robust Automatic Speech Recognition (ASR) based on parallel branch Hidden Markov Model (HMM) structure with a novel approach for robust speech recognition. Automatic Speech Recognition applications such as voice command and control, audio indexing, speech-to-speech translation, do not usually work well in noisy environments. In this paper, we present the characteristics of a novel model by exploring vibrocervigraphic and electromyographic ASR methods and some other effective approaches to achieve the best results. By employing the proposed model, we obtain the word error rate, available bandwidths with cutoff frequencies, word recognition rate, etc. This paper includes advanced front-end processing with less computational requirements and a statistical modeling for large-vocabulary myoelectric speech. Therefore parameters estimation of ASR system like Mel-Frequency Cepstral Coefficients (MFCC) are investigated to create the statistically optimized model.