<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>k</italic>-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.</p>
</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.

Investigation of the Lombard Effect Based on a Machine Learning Approach

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Investigation of the Lombard Effect Based on a Machine Learning Approach"}

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...

Investigation of the Lombard Effect Based on a Machine Learning Approach

Pubblicato online: 21 set 2023

Pagine: 479 - 492

Ricevuto: 05 ago 2022

Accettato: 03 mar 2023

DOI: https://doi.org/10.34768/amcs-2023-0035

Parole chiave
Lombard effect, speech detection, noise signal, self-similarity matrix, convolutional neural network

© 2023 Gražina Korvel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Investigation of the Lombard Effect Based on a Machine Learning Approach

Pubblicato online: 21 set 2023

Pagine: 479 - 492

Ricevuto: 05 ago 2022

Accettato: 03 mar 2023

DOI: https://doi.org/10.34768/amcs-2023-0035

Parole chiaveLombard effect, speech detection, noise signal, self-similarity matrix, convolutional neural network

© 2023 Gražina Korvel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Lombard effect, speech detection, noise signal, self-similarity matrix, convolutional neural network