Applying the Lombard Effect to Speech-in-Noise Communication

被引:0
|
作者
Korvel, Grazina [1 ]
Kakol, Krzysztof [2 ]
Treigys, Povilas [1 ]
Kostek, Bozena [3 ]
机构
[1] Vilnius Univ, Inst Data Sci & Digital Technol, LT-08412 Vilnius, Lithuania
[2] PGS Software, PL-50086 Wroclaw, Poland
[3] Gdansk Univ Technol, Fac Elect Telecommun & Informat, Audio Acoust Lab, PL-80233 Gdansk, Poland
关键词
Lombard effect; noise background; Structural SIMilarity (SSIM) index; RMSE (Root Mean Square Error); dHash (Difference Hash); QUALITY ASSESSMENT;
D O I
10.3390/electronics12244933
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study explored how the Lombard effect, a natural or artificial increase in speech loudness in noisy environments, can improve speech-in-noise communication. This study consisted of several experiments that measured the impact of different types of noise on synthesizing the Lombard effect. The main steps were as follows: first, a dataset of speech samples with and without the Lombard effect was collected in a controlled setting; then, the frequency changes in the speech signals were detected using the McAulay and Quartieri algorithm based on a 2D speech representation; next, an average formant track error was computed as a metric to evaluate the quality of the speech signals in noise. Three image assessment methods, namely the SSIM (Structural SIMilarity) index, RMSE (Root Mean Square Error), and dHash (Difference Hash) were used for this purpose. Furthermore, this study analyzed various spectral features of the speech signals in relation to the Lombard effect and the noise types. Finally, this study proposed a method for automatic noise profiling and applied pitch modifications to neutral speech signals according to the profile and the frequency change patterns. This study used an overlap-add synthesis in the STRAIGHT vocoder to generate the synthesized speech.
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页数:18
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