Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective
This work addresses accuracy issues in ASR for conversational speech, specifically for French users, but is incremental as it focuses on a narrow case study without broad improvements.
The paper tackled the problem of automatic speech recognition for conversational French by analyzing common errors, particularly homophones, to provide insights into transcription accuracy, though it did not report specific numerical results.
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.