Perceptual Features Based Isolated Digit And Continuous Speech Recognition Using Iterative Clustering Approach

The main objective of this paper is to explore the effectiveness of perceptual features for performing isolated digits and continuous speech recognition. The proposed perceptual features are captured and training models are developed by K-means clustering procedure. Speech recognition system is evaluated on clean and noisy test speeches and the experimental results reveal the performance of the proposed algorithm in recognizing digits and continuous speeches based on minimum distance between test features and clusters. Performance of these features is tested on speeches randomly chosen from "TI Digits_1", "TI Digits_2" and "TIMIT" databases. These perceptual features are evaluated with accuracy of 91% for speaker independent isolated digit recognition, 99.5% for clean continuous speech recognition and 87% for noisy continuous speech recognition. In this work, F-ratio is computed as a theoretical measure to validate the experimental results and chi(2) distribution is used to justify the good experimental results statistically.