Speaker Verification-Based Evaluation of Single-Channel Speech Separation

2021 ◽  
Author(s):  
Matthew Maciejewski ◽  
Shinji Watanabe ◽  
Sanjeev Khudanpur
Keyword(s):  
Single Channel ◽  
Speaker Verification ◽  
Speech Separation

Learning a Discriminative Dictionary for Single-Channel Speech Separation

2014 ◽  
Vol 22 (7) ◽  
pp. 1130-1138 ◽  
Author(s):  
Guangzhao Bao ◽  
Yangfei Xu ◽  
Zhongfu Ye
Keyword(s):  
Single Channel ◽  
Speech Separation

Performance comparison of HMM and VQ based single channel speech separation

2009 ◽  
Author(s):  
M. H. Radfar ◽  
W. -Y. Chan ◽  
R. M. Dansereau ◽  
W. Wong
Keyword(s):  
Single Channel ◽  
Performance Comparison ◽  
Speech Separation

scholarly journals An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dazhi Jiang ◽  
Zhihui He ◽  
Yingqing Lin ◽  
Yifei Chen ◽  
Linyan Xu
Keyword(s):  
Internet Of Things ◽  
Speech Signal ◽  
Single Channel ◽  
Low Noise ◽  
Sensor Data ◽  
The Internet ◽  
Speech Separation ◽  
Adaptive Wavelet ◽  
Enhancement Technique ◽  
The Internet Of Things

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

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