Browsing by Author "Sabitha P V"

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    ISOLATED TAMIL WORDS SPEECH RECOGNITION USING LINEAR PREDICTIVE CODING AND NEURAL NETWORKS
    (2012-12) Sabitha P V; Karpagavalli S
    Speech Recognition is the ability of a computer to recognize general, naturally flowing utterances from a wide variety of users. In recent years, with the new generation of computing technology, speech technology becomes the next major innovation in man-machine interaction. Automatic Speech Recognition (ASR) system takes a human speech utterance as an input and returns a string of words as output. Research on speech recognition has led to variety of applications like hands free and eyes free applications, voice user interfaces, simple data entry, forensic applications, voice authentication, biometrics, robotics, air traffic controllers, preparation of medical reports, learning tools for handicapped, and reading tools for blind people. Even though research in speech recognition in English language attained certain maturity, speech interfaces in Indian languages still in the startup level. Tamil is one of the widely spoken Indian languages of the world with more than 77 million speakers. Speech interfaces in Indian languages will enable the people in various semiurban and rural parts of India to use telephones and Internet services. In the proposed work, isolated Tamil words speech recognition interface is designed using neural network algorithm. To design the system, a dataset of 10 Tamil words uttered by 20 speakers each word 5 times has been prepared. Linear predictive coding of order 8 is used for feature extraction. Back-propagation training is carried with the feature vectors extracted using LPC from the speech files in the dataset. Multilayer Perceptron algorithm in neural network is employed for recognition of the words using the trained model. An interface also designed to recognize the Tamil words uttered by the user. The average recognition rate of the system is 93.6% and for few words it gives 100% accuracy. The performance of the system is measured using word recognition rate and word error rate
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    SCALY NEURAL NETWORKS FOR SPEECH RECOGNITION USING DTW AND TIME ALIGNMENT ALGORITHMS
    (International Journal of Scientific and Research Publications,, 2012-10) Sabitha P V; Karpagavalli S
    Speech recognition has been an active research topic for more than 50 years. Interacting with the computer through speech is one of the active scientific research fields particularly for the disable community who face variety of difficulties to use the computer. Such research in Automatic Speech Recognition (ASR) is investigated for different languages because each language has its specific features. Neural Networks are, in essence, biologically inspired networks since they are based on the current understanding of the biological nervous system. In essence they are comprised of a network of densely interconnected simple processing elements, which perform in a manner analogous to the most development of neural networks, and a basic introduction to their theory is outlined in this elementary functions of a biological neuron. Reduced connectivity neural networks are discussed and the scaly architecture neural network is described. Various algorithms are available to perform this time alignment of the input pattern to the neural network and the performance of the neural network is dependent upon the performance of the time alignment algorithm used. In this chapter, the various types of time alignment algorithms are described and their operation is outlined in detail.