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Abstract
The Lombard effect is an involuntary increase in the speaker’s pitch, intensity, and duration in the presence of noise. It makes it possible to communicate in noisy environments more effectively. This study aims to investigate an efficient method for detecting the Lombard effect in uttered speech. The influence of interfering noise, room type, and the gender of the person on the detection process is examined. First, acoustic parameters related to speech changes produced by the Lombard effect are extracted. Mid-term statistics are built upon the parameters and used for the self-similarity matrix construction. They constitute input data for a convolutional neural network (CNN). The self-similarity-based approach is then compared with two other methods, i.e., spectrograms used as input to the CNN and speech acoustic parameters combined with the k-nearest neighbors algorithm. The experimental investigations show the superiority of the self-similarity approach applied to Lombard effect detection over the other two methods utilized. Moreover, small standard deviation values for the self-similarity approach prove the resulting high accuracies.
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Authors (4)
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Grazina Korvel
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Povilas Treigys dr
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Krzysztof Kąkol mgr inż.
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.34768/amcs-2023-0035
- License
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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International Journal of Applied Mathematics and Computer Science
no. 33,
pages 479 - 492,
ISSN: 1641-876X - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Korvel G., Treigys P., Kąkol K., Kostek B.: INVESTIGATION OF THE LOMBARD EFFECT BASED ON A MACHINE LEARNING APPROACH// International Journal of Applied Mathematics and Computer Science -,iss. 33/3 (2023), s.479-492
- DOI:
- Digital Object Identifier (open in new tab) 10.34768/amcs-2023-0035
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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