Audio processing on constrained devices

Abstract

This thesis discusses the future of smart business applications on mobile phones and the integration of voice interface across several business applications. It proposes a framework that provides speech processing support for business applications on mobile phones. The framework uses Gaussian Mixture Models (GMM) for low-enrollment speaker recognition and limited vocabulary speech recognition. Algorithms are presented for pre-processing of audio signals into different categories and for start and end point detection. A method is proposed for speech processing that uses Mel Frequency Cepstral Coeffcients (MFCC) as primary feature for extraction. In addition, optimization schemes are developed to improve performance, and overcome constraints of a mobile phone. Experimental results are presented for some prototype applications that evaluate the performance of computationally expensive algorithms on constrained hardware. The thesis concludes by discussing the scope for improvement for the work done in this thesis and future directions in which this work could possibly be extended.