Analysis of French Phonetic Idiosyncrasies for Accent Recognition

TL;DR

This paper investigates the challenge of recognizing French accents in speech using spectrogram-based machine learning methods, highlighting the limitations of traditional techniques and proposing a CNN-based multi-class classification approach.

Contribution

It introduces a spectrogram-based CNN framework for French accent recognition and analyzes the impact of French phonetic idiosyncrasies on classification accuracy.

Findings

Classical machine learning techniques are insufficient for accurate accent classification.

CNN-based models improve accent recognition performance.

French phonetic idiosyncrasies significantly affect spectrogram features.

Abstract

Speech recognition systems have made tremendous progress since the last few decades. They have developed significantly in identifying the speech of the speaker. However, there is a scope of improvement in speech recognition systems in identifying the nuances and accents of a speaker. It is known that any specific natural language may possess at least one accent. Despite the identical word phonemic composition, if it is pronounced in different accents, we will have sound waves, which are different from each other. Differences in pronunciation, in accent and intonation of speech in general, create one of the most common problems of speech recognition. If there are a lot of accents in language we should create the acoustic model for each separately. We carry out a systematic analysis of the problem in the accurate classification of accents. We use traditional machine learning techniques and convolutional neural networks, and show that the classical techniques are not sufficiently efficient to solve this problem. Using spectrograms of speech signals, we propose a multi-class classification framework for accent recognition. In this paper, we focus our attention on the French accent. We also identify its limitation by understanding the impact of French idiosyncrasies on its spectrograms.