Large Vocabulary in Continuous Speech Recognition Using HMM and Normal Fit (original) (raw)
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International Journal of Physical Sciences, 2011
Most state of the art automatic speech recognition (ASR) systems are typically based on continuous Hidden Markov Models (HMMs) as acoustic modeling technique. It has been shown that the performance of HMM speech recognizers may be affected by a bad choice of the type of acoustic feature parameters in the acoustic front end module. For these reasons, we propose in this paper a dedicated isolated word recognition system based on HMMs which was carefully optimized specifically at the acoustic analysis and HMM acoustical modeling levels. Such conception was tested and valued on Hidden Markov model toolkit platform (HTK). Systems performances were evaluated using the TIMIT database. One comparative study was carried out using two types of speech analysis: The cepstral method referred to as Mel frequency cepstral coefficients (MFCC) and the perceptual linear predictive (PLP) coding are used for different tests so as to evaluate and reinforce our conception. The frame shift duration effect of the acoustic analysis as well as the addition of the dynamic coefficients of the acoustic parameters (MFCC and PLP) were carefully tested in order to look for high accuracy for our optimized isolated word recognition (IWR) system. Finally, various experiments related to the HMM topology have been carried out in order to get better recognition accuracies. In fact, the effect of some modeling parameters of HMM on the recognition accuracy of the IWR system such as the number of states as well as the number of Gaussian mixtures were analyzed in order to get the optimal HMM topology.
Continuous Density Hidden Markov Model for Hindi Speech Recognition
State of the art automatic speech recognition system uses Mel frequency cepstral coefficients as feature extractor along with Gaussian mixture model for acoustic modeling but there is no standard value to assign number of mixture component in speech recognition process.Current choice of mixture component is arbitrary with little justification. Also the standard set for European languages can not be used in Hindi speech recognition due to mismatch in database size of the languages.The parameter estimation with too many or few component may inappropriately estimate the mixture model. Therefore, number of mixture is important for initial estimation of expectation maximization process. In this research work, we estimate number of Gaussian mixture component for Hindi database based upon the size of vocabulary.Mel frequency cepstral feature and perceptual linear predictive feature along with its extended variations with delta-delta-delta feature have been used to evaluate this number based on optimal recognition score of the system . Comparitive analysis of recognition performance for both the feature extraction methods on medium size Hindi database is also presented in this paper.HLDA has been used as feature reduction technique and also its impact on the recognition score has been highlighted here.
Speaker Dependent and Independent Isolated Hindi Word Recognizer using Hidden Markov Model (HMM
Hindi is very complex language with large number of phonemes and being used with various ascents in different regions in India. In this manuscript, speaker dependent and independent isolated Hindi word recognizers using the Hidden Markov Model (HMM) is implemented, under noisy environment. For this study, a set of 10 Hindi names has been chosen as a test set for which the training and testing is performed. The scheme instigated here implements the Mel Frequency Cepstral Coefficients (MFCC) in order to compute the acoustic features of the speech signal. Then, K-means algorithm is used for the codebook generation by performing clustering over the obtained feature space. Baum Welch algorithm is used for re-estimating the parameters, and finally for deciding the recognized Hindi word whose model likelihood is highest, Viterbi algorithm has been implemented; for the given HMM. This work resulted in successful recognition with 98.6% recognition rate for speaker dependent recognition, for total of 10 speakers (6 male, 4 female) and 97.5% for speaker independent isolated word recognizer for 10 speakers (male).
Speech Recognition Using Monophone and Triphone Based Continuous Density Hidden Markov Models
Abstract—Speech Recognition is a process of transcribing speech to text. Phoneme based modeling is used where in each phoneme is represented by Continuous Density Hidden Markov Model. Mel Frequency Cepstral Coefficients (MFCC) are extracted from speech signal, delta and double-delta features representing the temporal rate of change of features are added which considerably improves the recognition accuracy. Each phoneme is represented by tristate Hidden Markov Model(HMM) with each state being represented by Continuous Density Gaussian model. As single mixture gaussian model do not represent the distribution of feature vectors in a better way, mixture splitting is performed successively in stages to eight mixture gaussian components. The multi-gaussian monophone models so generated do not capture all the variations of a phone with respect to its context, context dependent triphone models are build and the states are tied using decision tree based clustering. It is observed that recognition accuracy increases as the number of mixture components is increased and it works well for tied-state triphone based HMMs for large vocabulary. TIMIT Acoustic-Phonetic Continuous Speech Corpus is used for implementation. Recognition accuracy is also tested for our recorded speech.
Development of Application Specific Continuous Speech Recognition System in Hindi
Journal of Signal and Information Processing, 2012
Application specific voice interfaces in local languages will go a long way in reaching the benefits of technology to rural India. A continuous speech recognition system in Hindi tailored to aid teaching Geometry in Primary schools is the goal of the work. This paper presents the preliminary work done towards that end. We have used the Mel Frequency Cepstral Coefficients as speech feature parameters and Hidden Markov Modeling to model the acoustic features. Hidden Markov Modeling Tool Kit −3.4 was used both for feature extraction and model generation. The Julius recognizer which is language independent was used for decoding. A speaker independent system is implemented and results are presented.
— Speech recognition is the process of converting an acoustic waveform into the text similar to the information being conveyed by the speaker. In this paper implementation of isolated words and connected words Automatic Speech Recognition system (ASR) for the words of Hindi language will be discussed. The HTK (hidden markov model toolkit) based on Hidden Markov Model (HMM), a statistical approach, is used to develop the system. Initially the system is trained for 100 distinct Hindi words .This paper also describes the working of HTK tool, which is used in various phases of ASR system, by presenting a detailed architecture of an ASR system developed using various HTK library modules and tools. The recognition results will show that the overall system accuracy for isolated words is 95% and for connected words is 90%. Index Terms— HMM, HTK, Mel Frequency Cepstral Coefficient (MFCC), Automatic Speech Recognition (ASR), Hindi, Isolated word ASR, connected word ASR.
IEEE Transactions on Speech and Audio Processing, 1993
The author describes a large vocabulary, speaker-independent, continuous speech recognition system which is based on hidden Markov modeling (HMM) of phoneme-sized acoustic units using continuous mixture Gaussian densities. A bottom-up merging algorithm is developed for estimating the parameters of the mixture Gaussian densities, where the resultant number of mixture components is proportional to both the sample size and dispersion of training data. A compression procedure is developed to construct a word transcription dictionary from the acoustic-phonetic labels of sentence utterances. A modified word-pair grammar using context-sensitive grammatical parts is incorporated to constrain task difficulty. The Viterbi beam search is used for decoding. The segmental K-means algorithm is implemented as a baseline for evaluating the bottom-up merging technique. The system has been evaluated on the TIMIT database (1990) for a vocabulary size of 853. For test set perplexities of 24, 104, and 853, the decoding word accuracies are 90.9%, 86.0%, and 62.9%, respectively. For the perplexity of 104, the decoding accuracy achieved by using the merging algorithm is 4.1% higher than that using the segmental K-means (22.8% error reduction), and the decoding accuracy using the compressed dictionary is 3.0% higher than that using a standard dictionary (18.1% error reduction)
Specifics of Hidden Markov Model Modifications for Large Vocabulary Continuous Speech Recognition
2003
Abstract. Specifics of hidden Markov model-based speech recognition are investigated. Influ-ence of modeling simple and context-dependent phones, using simple Gaussian, two and three-component Gaussian mixture probability density functions for modeling feature distribution, and incorporating language model are discussed. Word recognition rates and model complexity criteria are used for evaluating suitability of these modifications for practical applications. Development of large vocabulary continuous speech recognition system using HTK toolkit and WSJCAM0 English speech corpus is described. Results of experimental investigations are presented. Key words: large vocabulary continuous speech recognition, hidden Markov model, Viterbi
Large Vocabulary Isolated Word Recognition Using Syllable, HMM and Normal Fit
this paper addresses the problem of large vocabulary speaker dependent isolated Kannada words recognition using the syllables, Hidden Markov Model (HMM) and Normal fit method. This experiment has covered 5.5 million words among the 10 million words from Hampi text corpus. Here 3-state Baum–Welch algorithm is used for training. For the 2 successor outputted λ(A, B, pi) is combined and passed into normal fit, the outputted normal fit parameter is labelled has syllable or sub-word. Our model is compared with Gaussian Mixture Model and HMM (3-state Baum–Welch algorithm). This paper clearly shows that for normal fit applied for HMM will reduce the memory size while building the speech models and works with excellent recognition rate. The average WRR is 91.22% and average WER is 8.78%. All computations are done using mat lab.