- Tytuł:
- Investigation of the Lombard effect based on a machine learning approach
- Autorzy:
-
Korvel, Gražina
Treigys, Povilas
Kąkol, Krzysztof
Kostek, Bożena - Powiązania:
- https://bibliotekanauki.pl/articles/24200693.pdf
- Data publikacji:
- 2023
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
Lombard effect
speech detection
noise signal
self similarity matrix
convolutional neural network
efekt Lombarda
wykrywanie mowy
sygnał szumowy
sieć neuronowa konwolucyjna - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 479--492
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł