Savitzky-Golay Filtering and Improved Energy Entropy for Speech Endpoint Detection under Low SNR

In this paper, we propose a new method using teager energy operator and entropy to solve endpoint detection problem in noisy environment. With the teager energy operator, it is sensitive on AM and FM signal and noise suppression capability on noisy speech signal, calculate teager energy of noisy speech signal. According to the different teager energy probability distribution between noise and speech signal, teager energy entropy is different. Set two soft thresholds of four states to detect the endpoint of noisy speech signal. The simulation shows that the method has good effect of endpoint detection in low SNR conditions, the simulation results show that teager energy entropy of speech signal endpoint detection in noisy environments is feasible and effective, and improves the reliability of endpoint detection.

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Telephone Speech Endpoint Detection using Mean-Delta Feature

Cybernetics and Information Technologies ◽

10.2478/cait-2014-0025 ◽

2014 ◽

Vol 14 (2) ◽

pp. 127-139 ◽

Cited By ~ 3

Author(s):

Atanas Ouzounov

Keyword(s):

Dynamic Time Warping ◽

Speaker Verification ◽

Verification Task ◽

Endpoint Detection ◽

Time Warping ◽

Energy Entropy ◽

Telephone Speech ◽

Dynamic Time ◽

Teager Energy ◽

Speech Endpoint Detection

Abstract In the study the efficiency of three features for trajectory-based endpoint detection is experimentally evaluated in the fixed-text Dynamic Time Warping (DTW) - a based speaker verification task with short phrases of telephone speech. The employed features are Modified Teager Energy (MTE), Energy-Entropy (EE) feature and Mean-Delta (MD) feature. The utterance boundaries in the endpoint detector are provided by means of state automaton and a set of thresholds based only on trajectory characteristics. The training and testing have been done with noisy telephone speech (short phrases in Bulgarian language with length of about 2 s) selected from BG-SRDat corpus. The results of the experiments have shown that the MD feature demonstrates the best performance in the endpoint detection tests in terms of the verification rate.

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An adaptive speech endpoint detection method in low SNR environments

International Journal of Speech Technology ◽

10.1007/s10772-017-9432-2 ◽

2017 ◽

Vol 20 (3) ◽

pp. 651-658 ◽

Cited By ~ 2

Author(s):

Linhui Sun ◽

Min Su ◽

Zhenzhen Yang

Keyword(s):

Detection Method ◽

Endpoint Detection ◽

Low Snr ◽

Speech Endpoint Detection

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Speech endpoint detection with low SNR based on HHTSM

2013 IEEE 11th International Conference on Electronic Measurement & Instruments ◽

10.1109/icemi.2013.6743066 ◽

2013 ◽

Author(s):

Liu Baisen ◽

Zhang Ye ◽

Zhang Wulin

Keyword(s):

Endpoint Detection ◽

Low Snr ◽

Speech Endpoint Detection

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Toeplitz Robust Noisy Speech Endpoint Detection

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.198-199.1462 ◽

2012 ◽

Vol 198-199 ◽

pp. 1462-1468

Author(s):

Jing Fang Wang

Keyword(s):

Detection Threshold ◽

Low Noise ◽

Detection Methods ◽

Endpoint Detection ◽

Low Snr ◽

Voice Signal ◽

The Matrix ◽

Feature Based ◽

The Cost ◽

Speech Endpoint Detection

In this paper, under the conditions of low SNR speech endpoint detection, a feature based on the maximum value of Toeplitz Noise endpoint detection methods. Terms of the method of spectrum from the corresponding sequences with a symmetric Toeplitz matrix constructed using the maximum eigenvalue of the matrix information on the voice signal for dual endpoint detection threshold. New algorithm has been tested to effectively distinguish between speech and noise, low-noise in different environmental conditions has good robustness. With the recent recursive signal analysis methods, the accuracy is higher. The algorithm to calculate the cost of a small, real good, simple and easy to implement.

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Study on speech endpoint detection algorithm based on wavelet energy entropy

2016 Chinese Control and Decision Conference (CCDC) ◽

10.1109/ccdc.2016.7531677 ◽

2016 ◽

Cited By ~ 1

Author(s):

Yali Cao ◽

Jing Gao ◽

Guang Yang

Keyword(s):

Detection Algorithm ◽

Endpoint Detection ◽

Wavelet Energy ◽

Energy Entropy ◽

Wavelet Energy Entropy ◽

Speech Endpoint Detection

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Speech Endpoint Detection in Noisy Environment Based on the Ensemble Empirical Mode Decomposition

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.2-3.135 ◽

2011 ◽

Vol 2-3 ◽

pp. 135-139

Author(s):

Jing Jiao Li ◽

Dong An ◽

Jiao Wang ◽

Chao Qun Rong

Keyword(s):

Empirical Mode Decomposition ◽

Speech Signal ◽

Recognition System ◽

Ensemble Empirical Mode Decomposition ◽

Endpoint Detection ◽

Noisy Environment ◽

Low Snr ◽

Voice Signal ◽

Mode Decomposition ◽

Speech Endpoint Detection

Speech endpoint detection is one of the key problems in the practical application of speech recognition system. In this paper, speech signal contained chirp is decomposed into several intrinsic mode function (IMF) with the method of ensemble empirical mode decomposition (EEMD). At the same time, it eliminates the modal mix superposition phenomenon which usually comes out in processing speech signal with the algorithm of empirical mode decomposition (EMD). After that, selects IMFs contained major noise through the adaptive algorithm. Finally, the IMFs and speech signal contained chirp are input into the independent component analysis (ICA) and pure voice signal is separated out. The accuracy of speech endpoint detection can be improved in this way. The result shows that the new speech endpoint detection method proposed above is effective, and has strong anti-noises ability, especially suitable for the speech endpoint detection in low SNR.

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