Realization of Hidden Markov Model for English Digit Recognition (original) (raw)
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Speech Recognition Using Hidden Markov Model Algorithm
Speech recognition applications are becoming more useful nowadays. With growth in the needs for embedded computing and the demand for emerging embedded platforms, it is required that speech recognition systems are available but speech recognition software being closed source cannot be used easily for implementation of speech recognition based devices. Aim To implement English words speech recognition system using Matlab (GUI). This work is based on Hidden Markov Model, which provides a highly reliable way for recognizing speech. Training data such as words like go up, go right, open, close etc. records in audacity open source; the system will test it with data record and display it in edit text box.
Speaker independent isolated digit recognition using hidden Markov models
The Journal of the Acoustical Society of America, 1982
A method for speaker independent isolated digit recognition based on modeling entire words as discrete probabilistic functions of a Markov process is described. Training is a three-part process comprising conventional methods of linear prediction analysis and vector quantization of the LPCs followed by an algorithm [L. E. Baum, Inequalities 3, 1–8 (1972)] for estimating the parameters of a hidden Markov process. Recognition utilizes linear prediction and vector quantization steps prior to maximum likelihood classification based on the Viterbi algorithm [A. J. Viterbi, IEEE Trans. Inf. Theo. IT-13, 260–269 (1967)]. After training based on a 1000-token set, recognition experiments were conducted on a separate 1000-token test set obtained from 100 new talkers. In this test a 3.5% error rate was observed which is comparable to that measured in an identical test of an LPC/DTW system [L. R. Rabiner et al., IEEE Trans. Acoust. Speech Signal Process. ASSP-37, 336–349 (1979)]. The computatio...
A Feature Based Classification and Analysis of Hidden Markov Model in Speech Recognition
Cyber Intelligence and Information Retrieval
Speech recognition is to change over the acoustical sign got from a spokesman or a phone which produces a lot of words. Speech recognition can also regardless called computer speech cognizance which means making the digital device understand what we are talking about. It helps users direct their systems for work, and avoids typing their work because a system can write words faster than a human being. There are several Hmm based models have been developed by the researchers for speech recognition but due to daily advancement of the technology landscape still need a robust techniques in the field of speech recognition. Due to its significant ability, it get classified and with the help of them there are several speech recognition techniques has been developed. The comparative analysis of the various Hmm model shows their efficiency and proposed the effective model in field of speech recognition.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/speaker-independent-speech-recognition-of-english-digits-using-hybridized-vq-hmm-model-in-noisy-environment https://www.ijert.org/research/speaker-independent-speech-recognition-of-english-digits-using-hybridized-vq-hmm-model-in-noisy-environment-IJERTV3IS042350.pdf This paper provides an analysis of recognition rate of English digits ("one" to "ten") in noisy environment. Automatic Speech Recognition (ASR) is not a new topic, but when deal with noisy environment and speaker independent recognition system, then it's requires lot of improvement. ASR accuracy is maximized by maximizing the word recognition rate. In this paper, pattern recognition approach is used i.e. based on template matching. Hidden Markov Models is one of the best pattern recognition approaches. In previous work, recognition rate is 67% for speaker independent in noisy environment. This paper is based on hybridized model of Vector quantization and Hidden Markov Model. Vector quantization is used to quantize the data vector within certain limit using K-Means algorithm. The Mel-frequency cepstral coefficients (MFCC) are used as a feature extraction approach to extract the feature of input analog speech signal. Experimental results show the improvement in recognition rate is up to 81.8 % with proposed hybridized VQ-HMM model.
International Journal of Computer Applications, 2013
The ability of a reader to recognize written words correctly, virtually and effortlessly is defined as Word Recognition or Isolated Word Recognition. It will recognize each word from their shape. Speech Recognition is the operating system which enablesto convert spoken words to written text which is called as Speech to Text (STT) method. Usual Method used in Speech Recognition (SR) is Neural Network, Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). The widely used technique for Speech Recognition is HMM. Hidden Markov Model assumes that successive acoustic features of a spoken word are state independent. The occurrence of one feature is independent of the occurrence of the others state. Here each single unit of word is considered as state. Based upon the probability of the state it generates possible word sequence for the spoken word. Instead of listening to the speech, the generated sequence of text can be easily viewed. Each word is recognized from their shape. People with hearing impaired can make use of this Speech Recognition.
Recognition of Isolated Digits Using HMM and Harmonic Noise Model
— In this paper we study an automatic speech recognition system formed by the association of Harmonic Noise Model (HNM) and Hidden Markov model (HMM). The HNM algorithm is used to extract the acoustic parameters and the HMM model is used to train and recognize this parameters. The experiments are performed on the spoken Arabic digit to measure the performance of the implemented recognition system.
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.
HMM-based Automatic Speech Recognition Systems: A survey
2017
Natural language processing enables computer and machines to understand and speak human languages. Speech recognition is a process in which computer understand the human language and processes further instructions as per recognition of the human language. The human language varies so the machine or computer needs entirely different algorithms as the human languages differ in various aspects, such as sounds, phonemes, words, meanings and much more. Understanding human language is a challenging job and for this purpose Hidden Markov Models are used commonly as they possess promising results in understanding human language. A survey of various researches employing Hidden Markov models is presented to highlight the importance of HMM in the process of speech recognition.
Speech is the most complex part or component of human intelligence and for that matter speech signal processing is very important. The variability of speech is very high, and this makes speech recognition difficult. Other factors like dialects, speech duration, context dependency, different speech speed, speaker differentiation, environment and locality all add to the difficulty in speech processing. The absence of distinct boundaries between tones or words causes additional problems. Speech has speaker dependent characteristics, so that no one can reproduce or repeat phrases in the same way as another. Nevertheless, a speech recognition system should be able to model and recognize the same words and phrases absolutely. Digital signal processors (DSP) are often used in speech signal processing systems to control these complexities. This paper presents a Hidden Markov Model (HMM) based speech signal modulation through the application of the Baum-Welch, Forward-Backward and Viterbi algorithms. The system was implemented using a 16-bit floating point DSP (TMS320C6701) from Texas instruments and the vocabulary was trained using the Microsoft Hidden Markov Model Toolkit (HTK). The proposed system achieved about 79% correct word recognition which represents approximately 11,804 correct words recognized out of a total of 14960 words provided. This result indicates that the proposed model accuracy and speaker independent system has a very good evaluation score, and thus can be used to aid dictation for speech impaired persons and applications in real time with a 10 ms data exchange rate.