Efficient spectral envelope estimation from harmonic speech signals

2012 ◽  
Vol 48 (16) ◽  
pp. 1019-1021 ◽  
Author(s):  
D. Erro ◽  
E. Navas ◽  
I. Sainz ◽  
I. Hernaez
Keyword(s):  
Spectral Envelope ◽  
Speech Signals

scholarly journals Voice Conversion Using a Perceptual Criterion

2020 ◽  
Vol 10 (8) ◽  
pp. 2884
Author(s):  
Ki-Seung Lee
Keyword(s):  
Voice Conversion ◽  
Spectral Envelope ◽  
Perceptual Quality ◽  
Speech Signals ◽  
Perceptual Evaluation ◽  
Perceptual Distance ◽  
Modification Factor ◽  
Maximum Improvement ◽  
Validation Tests ◽  
Conversion Rules

In voice conversion (VC), it is highly desirable to obtain transformed speech signals that are perceptually close to a target speaker’s voice. To this end, a perceptually meaningful criterion where the human auditory system was taken into consideration in measuring the distances between the converted and the target voices was adopted in the proposed VC scheme. The conversion rules for the features associated with the spectral envelope and the pitch modification factor were jointly constructed so that perceptual distance measurement was minimized. This minimization problem was solved using a deep neural network (DNN) framework where input features and target features were derived from source speech signals and time-aligned version of target speech signals, respectively. The validation tests were carried out for the CMU ARCTIC database to evaluate the effectiveness of the proposed method, especially in terms of perceptual quality. The experimental results showed that the proposed method yielded perceptually preferred results compared with independent conversion using conventional mean-square error (MSE) criterion. The maximum improvement in perceptual evaluation of speech quality (PESQ) was 0.312, compared with the conventional VC method.

Acoustic Analysis of Emotional Expressions Present in Speech Signals.

1999 ◽  
Vol 53 (5) ◽  
pp. 769-772
Author(s):  
Yoshito Mekada ◽  
Miyuki Mukasa ◽  
Hiroshi Hasegawa ◽  
Masao Kasuga ◽  
Shuichi Matsumoto ◽  
...  
Keyword(s):  
Acoustic Analysis ◽  
Emotional Expressions ◽  
Speech Signals
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