# Analysis of French Phonetic Idiosyncrasies for Accent Recognition

arXiv: https://arxiv.org/abs/2110.09179 · HTML: https://mlsanity.com/paper/2110.09179/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Using GANs to Augment Data for Cloud Image Segmentation Task](https://mlsanity.com/paper/2106.03064/) (2021)
- [Machine-Learning Techniques for Detecting Attacks in SDN](https://mlsanity.com/paper/1910.00817/) (2019)
- [Ontology Modeling for Decentralized Household Energy Systems](https://mlsanity.com/paper/2108.01504/) (2021)
- [TEST: A Terminology Extraction System for Technology Related Terms](https://mlsanity.com/paper/1812.09541/) (2018)
- [Identifying Candidate Spaces for Advert Implantation](https://mlsanity.com/paper/1910.03227/) (2019)

## Built on by (most cited first, 1 of 1)

- [Frequency-centroid features for word recognition of non-native English speakers](https://mlsanity.com/paper/2206.07176/) (2022)

## Abstract (arXiv)

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.