Speaker based Language Independent Isolated Speech Recognition System

This paper presents a speaker based Language Independent Isolated Speech Recognition System (LIISRS). The most popular feature extraction technique Mel Frequency Cepstral Coefficients (MFCC) is used for training the system. Representative specific features are identified using K-Means algorithm. Distortion measure is calculated using Euclidian distance function. Pitch contour characteristics are used to identify the language specific features. Decision rules are formed to recognize language and speech of the given input. Thus, the proposed system not only recognizes the speech but also the language in which the speech is uttered. The result shows a satisfactory performance when the training is carried using native language speakers. Digits from one to ten of seven different languages are taken as training samples. Results obtained using 12 MFCC features for overall word level accuracy is 90.02% and language recognition accuracy is 97.14%.