Abstract
The automatic speech recognition (ASR) is an active field of research. The performance of the ASR can be degraded due to various features like environmental noise, channel distortion and speech rate variability. The speech rate variability is one of the important features that affect the accuracy of the speech recognition system (SRS). In this research work, the speech signal is categorized as slow, normal and fast speech using features like the sound intensity level, time duration and root mean square. This paper addresses the enhancement of the performance of a SRS by applying time normalization to the speech signal. The comparison of the proposed Model and baseline syllable based SRS is done.
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