Automatic Speech Recognition (ASR) systems have achieved remarkable performance on widely used benchmarks such as LibriSpeech and Fleurs. However, these benchmarks do not adequately reflect the complexities of real-world conversational environments, where speech is often unstructured and contains disfluencies such as pauses, interruptions, and diverse accents. In this study, we introduce a multilingual conversational dataset, derived from TalkBank, consisting of unstructured phone conversation between adults. Our results show a significant performance drop across various state-of-the-art ASR models when tested in conversational settings. Furthermore, we observe a correlation between Word Error Rate and the presence of speech disfluencies, highlighting the critical need for more realistic, conversational ASR benchmarks.
@article{arxiv.2409.12042,
title = {ASR Benchmarking: Need for a More Representative Conversational Dataset},
author = {Gaurav Maheshwari and Dmitry Ivanov and Théo Johannet and Kevin El Haddad},
journal= {arXiv preprint arXiv:2409.12042},
year = {2024}
}