ROBUST RECOGNITION OF SMALL-VOCABULARY TELEPHONE- QUALITY SPEECH

Considerable progress has been made in the field of automatic speech recognition in recent years, especially for high-quality (full bandwidth and noise-free) speech. However, good recognition accuracy is difficult to achieve when the incoming speech is passed through a telephone channel. At the same time, the task of speech recognition over telephone lines is growing in importance, as the number of applications of spoken language processing involving telephone speech increases every day. The paper presents our recent work on developing a robust speaker-independent isolated-spoken word recognition system based on a hybrid approach (classic – artificial neural network). A number of experiments are described and compared in order to evaluate different analysis and recognition techniques that are best suited for a telephone-speech recognition task. In particular, we address the use of RASTA processing (i.e., filtering the temporal trajectories of speech parameters) for increasing the recognition accuracy. Also, we propose a method based on the adaptive filter theory for producing simulated telephone data starting from clean speech databases