  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.
