SPGISpeech 2.0: Transcribed multi-speaker financial audio for speaker-tagged transcription
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918117499731968 |
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| author | Grossman, Raymond Park, Taejin Dhawan, Kunal Titus, Andrew Zhi, Sophia Shchadilova, Yulia Wang, Weiqing Balam, Jagadeesh Ginsburg, Boris |
| author_facet | Grossman, Raymond Park, Taejin Dhawan, Kunal Titus, Andrew Zhi, Sophia Shchadilova, Yulia Wang, Weiqing Balam, Jagadeesh Ginsburg, Boris |
| contents | We introduce SPGISpeech 2.0, a dataset suitable for speaker-tagged transcription in the financial domain. SPGISpeech 2.0 improves the diversity of applicable modeling tasks while maintaining the core characteristic of the original SPGISpeech dataset: audio snippets and their corresponding fully formatted text transcriptions, usable for end-to-end automatic speech recognition (ASR). SPGISpeech 2.0 consists of 3,780 additional hours of professionally transcribed earnings calls. Furthermore, the dataset contains call and speaker information for each audio snippet facilitating multi-talker ASR. We validate the utility of SPGISpeech 2.0 through improvements in speaker-tagged ASR performance of popular speech recognition models after fine-tuning on SPGISpeech 2.0. Released free for non-commercial use, we expect SPGISpeech 2.0 to foster advancements in speech recognition technologies and inspire a wide range of research applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05554 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPGISpeech 2.0: Transcribed multi-speaker financial audio for speaker-tagged transcription Grossman, Raymond Park, Taejin Dhawan, Kunal Titus, Andrew Zhi, Sophia Shchadilova, Yulia Wang, Weiqing Balam, Jagadeesh Ginsburg, Boris Sound Computation and Language Audio and Speech Processing We introduce SPGISpeech 2.0, a dataset suitable for speaker-tagged transcription in the financial domain. SPGISpeech 2.0 improves the diversity of applicable modeling tasks while maintaining the core characteristic of the original SPGISpeech dataset: audio snippets and their corresponding fully formatted text transcriptions, usable for end-to-end automatic speech recognition (ASR). SPGISpeech 2.0 consists of 3,780 additional hours of professionally transcribed earnings calls. Furthermore, the dataset contains call and speaker information for each audio snippet facilitating multi-talker ASR. We validate the utility of SPGISpeech 2.0 through improvements in speaker-tagged ASR performance of popular speech recognition models after fine-tuning on SPGISpeech 2.0. Released free for non-commercial use, we expect SPGISpeech 2.0 to foster advancements in speech recognition technologies and inspire a wide range of research applications. |
| title | SPGISpeech 2.0: Transcribed multi-speaker financial audio for speaker-tagged transcription |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.05554 |