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

Automatic Speech Recognition system is developed for recognizing the continuous and spontaneous Kannada speech sentences in clean and noisy environments. 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, Mel Frequency Cepstral Coefficients interms of WER under both clean and nosiy environmental conditions.