Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Robust Endpoint Detection for In-Car Speech Recognition
Chung-Ho Yang, Ming-Shiun Hsieh
Panasonic Taiwan Laboratories Co., Ltd., Taipei, Taiwan
The endpoint detection plays a significantly important role in the
front end processing of speech recognition. It is very difficult,
however, to precisely locate endpoints on the input utterance to
be free on non-speech regions because of unpredictable
background noise. This paper proposes a novel approach that
finds robust features for better endpoint detection in a noisy incar
environment. In the proposed method, we integrate both the
widely used energy and entropy to form a new feature
that possesses advantages of each individual while compensating
the drawback of each other. By monitoring the variation of the
extracted new features, more precise endpoints can be found.
Experimental results present that this algorithm outperforms the
energy-based algorithms in both accuracy of boundary point
detection and recognition performance under a real in-car noisy
environment. The result of accuracy improvement shows 10%
higher comparing with energy-based algorithm.
Yang, Chung-Ho / Hsieh, Ming-Shiun (2000):
"Robust endpoint detection for in-car speech recognition",
In ICSLP-2000, vol.2, 1061-1064.