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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00267 |
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| _version_ | 1866909645664157696 |
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| author | Xiao, Cihan Liang, Ruixing Zhang, Xiangyu Tiryaki, Mehmet Emre Bae, Veronica Shankar, Lavanya Yang, Rong Poon, Ethan Dupoux, Emmanuel Khudanpur, Sanjeev Perera, Leibny Paola Garcia |
| author_facet | Xiao, Cihan Liang, Ruixing Zhang, Xiangyu Tiryaki, Mehmet Emre Bae, Veronica Shankar, Lavanya Yang, Rong Poon, Ethan Dupoux, Emmanuel Khudanpur, Sanjeev Perera, Leibny Paola Garcia |
| contents | The success of large language models has driven interest in developing similar speech processing capabilities. However, a key challenge is the scarcity of high-quality spontaneous speech data, as most existing datasets contain scripted dialogues. To address this, we present a novel pipeline for eliciting and recording natural dialogues and release our dataset with 100+ hours of spontaneous speech. Our approach fosters fluid, natural conversations while encouraging a diverse range of topics and interactive exchanges. Unlike traditional methods, it facilitates genuine interactions, providing a reproducible framework for future data collection. This paper introduces our dataset and methodology, laying the groundwork for addressing the shortage of spontaneous speech data. We plan to expand this dataset in future stages, offering a growing resource for the research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CASPER: A Large Scale Spontaneous Speech Dataset Xiao, Cihan Liang, Ruixing Zhang, Xiangyu Tiryaki, Mehmet Emre Bae, Veronica Shankar, Lavanya Yang, Rong Poon, Ethan Dupoux, Emmanuel Khudanpur, Sanjeev Perera, Leibny Paola Garcia Computation and Language Sound Audio and Speech Processing The success of large language models has driven interest in developing similar speech processing capabilities. However, a key challenge is the scarcity of high-quality spontaneous speech data, as most existing datasets contain scripted dialogues. To address this, we present a novel pipeline for eliciting and recording natural dialogues and release our dataset with 100+ hours of spontaneous speech. Our approach fosters fluid, natural conversations while encouraging a diverse range of topics and interactive exchanges. Unlike traditional methods, it facilitates genuine interactions, providing a reproducible framework for future data collection. This paper introduces our dataset and methodology, laying the groundwork for addressing the shortage of spontaneous speech data. We plan to expand this dataset in future stages, offering a growing resource for the research community. |
| title | CASPER: A Large Scale Spontaneous Speech Dataset |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.00267 |