Abstract:
An endpoint detection in low signal-to-noise ratio (SNR) environment plays an important role in speech processing. In this paper, we propose an endpoint detection algorit...Show MoreMetadata
Abstract:
An endpoint detection in low signal-to-noise ratio (SNR) environment plays an important role in speech processing. In this paper, we propose an endpoint detection algorithm applying a novel feature parameter, called Spectrogram Boundary Factor (SBF), to improve the endpoint detection performance in noisy environments. In the first step, the time-frequency spectrogram is obtained from the noisy speech. Then we use the erosion algorithm and the dilation algorithm to attenuate the noise and enhance the speech banded structure on the spectrogram respectively. Finally, we calculate the SBF feature parameter after edge detection using the improved spectrogram first-order derivative. The experiments prove that using the proposed algorithm, the speech endpoint can be effectively detected even in a case of low SNR of 0 dB.
Published in: 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
Date of Conference: 15-17 October 2016
Date Added to IEEE Xplore: 16 February 2017
ISBN Information: