  Automatic speech recognition (ASR) meets more informal and free-form input
data as voice user interfaces and conversational agents such as the voice
assistants such as Alexa, Google Home, etc., gain popularity. Conversational
speech is both the most difficult and environmentally relevant sort of data for
speech recognition. In this paper, we take a linguistic perspective, and take
the French language as a case study toward disambiguation of the French
homophones. Our contribution aims to provide more insight into human speech
transcription accuracy in conditions to reproduce those of state-of-the-art ASR
systems, although in a much focused situation. We investigate a case study
involving the most common errors encountered in the automatic transcription of
French language.
