Presentation of K Nearest Neighbor Gaussian Interpolation and comparing it with Fuzzy Interpolation in Speech Recognition

A. Sayadiyan,; K. Badi,; M. Moin And N. Moghadam,

Presentation of K Nearest Neighbor Gaussian Interpolation and comparing it with Fuzzy Interpolation in Speech Recognition

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Abstract

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very high, especially in very large discrete utterance recognition problems. For real time implementation of very large discrete utterance recognition, we must use discrete density HMM (DDHMM). To increase the performance of DDHMM, one usual solution is fuzzy interpolation. In this study, we present a new method named Gaussian interpolation. We implemented and compared the performance of two types of interpolation methods for 1500 Persian speech command words. Results show that precision and flexibility of Gaussian interpolation is better thanthose of the fuzzy interpolation.

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