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ICASSP
2011
IEEE
12 years 8 months ago
Evaluation of objective measures for intelligibility prediction of HMM-based synthetic speech in noise
In this paper we evaluate four objective measures of speech with regards to intelligibility prediction of synthesized speech in diverse noisy situations. We evaluated three intell...
Cassia Valentini-Botinhao, Junichi Yamagishi, Simo...
SPEECH
2011
12 years 11 months ago
SNR loss: A new objective measure for predicting the intelligibility of noise-suppressed speech
Most of the existing intelligibility measures do not account for the distortions present in processed speech, such as those introduced by speech-enhancement algorithms. In the pre...
Jianfen Ma, Philipos C. Loizou
TASLP
2008
133views more  TASLP 2008»
13 years 4 months ago
Evaluation of Objective Quality Measures for Speech Enhancement
In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective ...
Yi Hu, Philipos C. Loizou
ICASSP
2009
IEEE
13 years 11 months ago
On the use of Bayesian modeling for predicting noise reduction performance
In speech enhancement applications, a validated metric of noise reduction performance is vital in the relative ranking of noise reduction algorithms and in enhancing the performan...
Nazanin Pourmand, David Suelzle, Vijay Parsa, Yi H...
INTERSPEECH
2010
12 years 11 months ago
Reducing musical noise in blind source separation by time-domain sparse filters and split bregman method
Musical noise often arises in the outputs of time-frequency binary mask based blind source separation approaches. Postprocessing is desired to enhance the separation quality. An e...
Wenye Ma, Meng Yu, Jack Xin, Stanley Osher