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Paper details
Number 3 - September 2023
Volume 33 - 2023
Investigation of the Lombard effect based on a machine learning approach
Gražina Korvel, Povilas Treigys, Krzysztof Kąkol, Bożena Kostek
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.
Keywords
Lombard effect, speech detection, noise signal, self-similarity matrix, convolutional neural network