An Effective Tamil Speech Word Recognition Technique with Aid of MFCC and HMM (Hidden Markov Model)

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Simply transcribing the speech without essentially knowing the meaning of the utterance is known as Speech recognition. The two can be combined, however the task described here is merely recognition. In this paper a new speech recognition method is proposed for Tamil language speech word recognition. Here, a HMM based recognition method is utilized. In the proposed method, initially preprocessing is performed to reduce the noise in the input speech signals. Then, MFCC feature vectors are extracted from the preprocessed speech signals and these extracted MFCC features are given to the HMM (Hidden Markov Model). Hidden Markov Model (HMM) is a natural and highly efficient statistical technique for automatic speech recognition. It was tested and proved substantially in a wide variety of applications. The model parameters of the HMM are useful in describing the behavior of the utterance. An HMM is a most dominant toll in the speech recognition process and this provides high accuracy results in the speech word recognition. Based on the input word features, the HMM model recognizes the input words more precisely. A set of input words are utilized in the speech recognition process and the result from this HMM guarantees the healthiness of the proposed technique. The implementation result shows the effectiveness of the proposed recognition method in recognizing the input speech words as well as the achieved improvement in their accuracy measure.
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Speech Recognition; Filtering; Gaussian Filter; MFCC Features; HMM

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