Spontaneous Informal Speech Dataset for Punctuation Restoration

• URL: http://arxiv.org/abs/2409.11241v1
• Date: Tue, 17 Sep 2024 14:43:14 GMT
• Title: Spontaneous Informal Speech Dataset for Punctuation Restoration
• Authors: Xing Yi Liu, Homayoon Beigi,
• Abstract summary: We introduce SponSpeech, a punctuation restoration dataset derived from informal speech sources. Our filtering pipeline examines the quality of both speech audio and transcription text. We also carefully construct a challenging" test set, aimed at evaluating models' ability to leverage audio information to predict otherwise grammatically ambiguous punctuation.
• Score: 0.8517406772939293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Presently, punctuation restoration models are evaluated almost solely on well-structured, scripted corpora. On the other hand, real-world ASR systems and post-processing pipelines typically apply towards spontaneous speech with significant irregularities, stutters, and deviations from perfect grammar. To address this discrepancy, we introduce SponSpeech, a punctuation restoration dataset derived from informal speech sources, which includes punctuation and casing information. In addition to publicly releasing the dataset, we contribute a filtering pipeline that can be used to generate more data. Our filtering pipeline examines the quality of both speech audio and transcription text. We also carefully construct a ``challenging" test set, aimed at evaluating models' ability to leverage audio information to predict otherwise grammatically ambiguous punctuation. SponSpeech is available at https://github.com/GitHubAccountAnonymous/PR, along with all code for dataset building and model runs.

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