An efficient framework for robust mobile speech recognition services

Abstract

A distributed framework for implementing automatic speech recognition (ASR) services on wireless mobile devices is presented. The framework is shown to scale easily to support a large number of mobile users connected over a wireless network and degrade gracefully under peak loads. The importance of using robust acoustic modeling techniques is demonstrated for situations when the use of specialized acoustic transducers on the mobile devices is not practical. It is shown that unsupervised acoustic normalization and adaptation techniques can reduce speech recognition word error rate (WER) by 30 percent. It is also shown that an unsupervised paradigm for updating and applying these robust modeling algorithms can be efficiently implemented within the distributed framework.