Robust Automatic Speech Recognition System for Kannada Speech Sentences in the Presence of Noise

Abstract

An Automatic Speech Recognition system is developed for recognizing the continuous and spontaneous Kannada speech sentences. The language models and acoustic models are constructed using Kaldi toolkit. The speech corpus is developed with the native female and male Kannada speakers and is partioned into training set and testing set. The Performance of the proposed system is analysed and evaluated using the metric Word Error Rate (WER). The Wavelet Packets amalgamated with Mel filter banks are utilized to perform feature vector generation. The proposed hand crafted features perform better than the baseline features such as Perceptual Linear Prediction(PLP), Mel Frequency Cepstral Coefficients(MFCC) interms of Word Error Rate(WER) under both controlled and uncontrolled conditions.