System and method for an endpoint detection of speech for improved speech recognition in noisy environment

Abstract

According to a disclosed embodiment, an endpointer determines the background energy of a first portion of a speech signal, and a cepstral computing module extracts one or more features of the first portion. The endpointer calculates an average distance of the first portion based on the features. Subsequently, an energy computing module measures the energy of a second portion of the speech signal, and the cepstral computing module extracts one or more features of the second portion. Based on the features of the second portion, the endpointer calculates a distance of the second portion. Thereafter, the endpointer contrasts the energy of the second portion with the background energy of the first portion, and compares the distance of the second portion with the distance of the first portion. The second portion of the speech signal is classified by the endpointer as speech or non-speech based on the contrast and the comparison.

Description

RELATED APPLICATIONS [0001] The present application claims the benefit of U.S. provisional application Ser. No. 60 / 272,956, filed Mar. 2, 2001, which is hereby fully incorporated by reference in the present application.BACKGROUND OF THE INVENTION [0002] 1. Field of the Invention [0003] The present invention relates generally to the field of speech recognition and, more particularly, speech recognition in noisy environments. [0004] 2. Related Art [0005] Automatic speech recognition (“ASR”) refers to the ability to convert speech signals into words, or put another way, the ability of a machine to recognize human voice. ASR systems are generally categorized into three types: speaker-independent ASR, speaker-dependent ASR and speaker-verification ASR. Speaker-independent ASR can recognize a group of words from any speaker and allow any speaker to use the available vocabularies after having been trained for a standard vocabulary. Speaker-dependent ASR, on the other hand, can identify a v...

AI Technical Summary

Problems solved by technology

Detecting when actual speech activity contained in an input speech signal begins and ends is a basic problem for all ASR systems, and it is well-recognized that proper detection is crucial for good speech recognition accuracy.

Thus, when actual speech activity is mistakenly classified by the endpointer as silence or non-speech, or when non-speech activity is erroneously grouped with speech, speech recognition accuracy is significantly diminished.

Another disadvantage of the conventional energy-based endpoint detection algorithm, such as the one utilized by endpoint detection system 100, is that it has little or no immunity to background noise.

Such errors lead to high false rejection rates, and reflect negatively on the overall performance of the ASR system.

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