The Performance of an Isolated Word Recognizer Using Noisy Speech.

1983 ◽  
Author(s):  
Gary Neben
Keyword(s):  
Noisy Speech ◽  
Isolated Word

Multi-resolution Gammachirp Envelope Distortion Index for Intelligibility Prediction of Noisy Speech

2018 ◽  
Author(s):  
Katsuhiko Yamamoto ◽  
Toshio Irino ◽  
Narumi Ohashi ◽  
Shoko Araki ◽  
Keisuke Kinoshita ◽  
...  
Keyword(s):  
Noisy Speech ◽  
Distortion Index

scholarly journals Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 434-455
Author(s):  
Sujan Kumar Roy ◽  
Kuldip K. Paliwal
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Linear Prediction ◽  
Real Life ◽  
Model Parameters ◽  
Noise Variance ◽  
Noisy Speech ◽  
Kalman Gain ◽  
Whitening Filter ◽  
Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

Nonlinear modeling and filtering by RBF network with application to noisy speech signal

1997 ◽  
Vol 101 (3-4) ◽  
pp. 177-185 ◽  
Author(s):  
Eiji Uchino ◽  
Shin Nakamura ◽  
Takeshi Yamakawa
Keyword(s):  
Speech Signal ◽  
Nonlinear Modeling ◽  
Noisy Speech ◽  
Rbf Network
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