ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition

• URL: http://arxiv.org/abs/2210.13352v1
• Date: Mon, 24 Oct 2022 15:58:48 GMT
• Title: ESB: A Benchmark For Multi-Domain End-to-End Speech Recognition
• Authors: Sanchit Gandhi, Patrick von Platen and Alexander M. Rush
• Abstract summary: Speech recognition systems require dataset-specific tuning. This tuning requirement can lead to systems failing to generalise to other datasets and domains. We introduce the End-to-end Speech Benchmark (ESB) for evaluating the performance of a single automatic speech recognition system.
• Score: 100.30565531246165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech recognition applications cover a range of different audio and text distributions, with different speaking styles, background noise, transcription punctuation and character casing. However, many speech recognition systems require dataset-specific tuning (audio filtering, punctuation removal and normalisation of casing), therefore assuming a-priori knowledge of both the audio and text distributions. This tuning requirement can lead to systems failing to generalise to other datasets and domains. To promote the development of multi-domain speech systems, we introduce the End-to-end Speech Benchmark (ESB) for evaluating the performance of a single automatic speech recognition (ASR) system across a broad set of speech datasets. Benchmarked systems must use the same data pre- and post-processing algorithm across datasets - assuming the audio and text data distributions are a-priori unknown. We compare a series of state-of-the-art (SoTA) end-to-end (E2E) systems on this benchmark, demonstrating how a single speech system can be applied and evaluated on a wide range of data distributions. We find E2E systems to be effective across datasets: in a fair comparison, E2E systems achieve within 2.6% of SoTA systems tuned to a specific dataset. Our analysis reveals that transcription artefacts, such as punctuation and casing, pose difficulties for ASR systems and should be included in evaluation. We believe E2E benchmarking over a range of datasets promotes the research of multi-domain speech recognition systems. ESB is available at https://huggingface.co/esb.

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