Speech endpoint detection in noisy environment using Spectrogram Boundary Factor

2016 ◽  
Author(s):  
Di Wu ◽  
Zhi Tao ◽  
Yuanbo Wu ◽  
Cheng Shen ◽  
Zhongzhe Xiao ◽  
...  
Keyword(s):  
Endpoint Detection ◽  
Noisy Environment ◽  
Speech Endpoint Detection

scholarly journals Speech Endpoint Detection Method Based on TEO in Noisy Environment

2012 ◽  
Vol 29 ◽  
pp. 2655-2660 ◽  
Author(s):  
Jie Li ◽  
Ping Zhou ◽  
Xinxing Jing ◽  
Zhiran Du
Keyword(s):  
Detection Method ◽  
Endpoint Detection ◽  
Noisy Environment ◽  
Speech Endpoint Detection

Speech Endpoint Detection Based on EMD and Spectral Entropy in Noisy Environments

2012 ◽  
Vol 229-231 ◽  
pp. 1296-1299 ◽  
Author(s):  
Yan Li Liu ◽  
De Xiang Zhang ◽  
Ming Wei Ji
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Endpoint Detection ◽  
Intrinsic Mode Functions ◽  
Noisy Environment ◽  
Spectral Entropy ◽  
Noisy Environments ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Speech Endpoint Detection

Accurate endpoint detection is crucial for speech recognition accuracy. A novel approach that finds robust features for endpoint detection based on the empirical mode decomposition (EMD) algorithm and spectral entropy in a noisy environment is proposed. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then spectral entropy can be used to extract the desired feature for IMF components. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experiments show that the proposed algorithm can suppress different noise types with different SNR, and the algorithm is robust in the real signal tests.

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

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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