SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909812310147072 |
|---|---|
| author | Bai, Bingsong Lu, Qihang Yang, Wenbing Sun, Zihan Hou, Yueran Jia, Peilei Pu, Songbai Fu, Ruibo Gao, Yingming Li, Ya Gao, Jun |
| author_facet | Bai, Bingsong Lu, Qihang Yang, Wenbing Sun, Zihan Hou, Yueran Jia, Peilei Pu, Songbai Fu, Ruibo Gao, Yingming Li, Ya Gao, Jun |
| contents | Paralinguistic sounds, like laughter and sighs, are crucial for synthesizing more realistic and engaging speech. However, existing methods typically depend on proprietary datasets, while publicly available resources often suffer from incomplete speech, inaccurate or missing timestamps, and limited real-world relevance. To address these problems, we propose an automated framework for generating large-scale paralinguistic data and apply it to construct the SynParaSpeech dataset. The dataset comprises 6 paralinguistic categories with 118.75 hours of data and precise timestamps, all derived from natural conversational speech. Our contributions lie in introducing the first automated method for constructing large-scale paralinguistic datasets and releasing the SynParaSpeech corpus, which advances speech generation through more natural paralinguistic synthesis and enhances speech understanding by improving paralinguistic event detection. The dataset and audio samples are available at https://github.com/ShawnPi233/SynParaSpeech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding Bai, Bingsong Lu, Qihang Yang, Wenbing Sun, Zihan Hou, Yueran Jia, Peilei Pu, Songbai Fu, Ruibo Gao, Yingming Li, Ya Gao, Jun Audio and Speech Processing Computation and Language I.2.7 Paralinguistic sounds, like laughter and sighs, are crucial for synthesizing more realistic and engaging speech. However, existing methods typically depend on proprietary datasets, while publicly available resources often suffer from incomplete speech, inaccurate or missing timestamps, and limited real-world relevance. To address these problems, we propose an automated framework for generating large-scale paralinguistic data and apply it to construct the SynParaSpeech dataset. The dataset comprises 6 paralinguistic categories with 118.75 hours of data and precise timestamps, all derived from natural conversational speech. Our contributions lie in introducing the first automated method for constructing large-scale paralinguistic datasets and releasing the SynParaSpeech corpus, which advances speech generation through more natural paralinguistic synthesis and enhances speech understanding by improving paralinguistic event detection. The dataset and audio samples are available at https://github.com/ShawnPi233/SynParaSpeech. |
| title | SynParaSpeech: Automated Synthesis of Paralinguistic Datasets for Speech Generation and Understanding |
| topic | Audio and Speech Processing Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2509.14946 |