Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916751792406528 |
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| author | Wang, Yuzhu Politis, Archontis Drossos, Konstantinos Virtanen, Tuomas |
| author_facet | Wang, Yuzhu Politis, Archontis Drossos, Konstantinos Virtanen, Tuomas |
| contents | This paper addresses the problem of single-channel speech separation, where the number of speakers is unknown, and each speaker may speak multiple utterances. We propose a speech separation model that simultaneously performs separation, dynamically estimates the number of speakers, and detects individual speaker activities by integrating an attractor module. The proposed system outperforms existing methods by introducing an attractor-based architecture that effectively combines local and global temporal modeling for multi-utterance scenarios. To evaluate the method in reverberant and noisy conditions, a multi-speaker multi-utterance dataset was synthesized by combining Librispeech speech signals with WHAM! noise signals. The results demonstrate that the proposed system accurately estimates the number of sources. The system effectively detects source activities and separates the corresponding utterances into correct outputs in both known and unknown source count scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16607 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers Wang, Yuzhu Politis, Archontis Drossos, Konstantinos Virtanen, Tuomas Audio and Speech Processing Sound This paper addresses the problem of single-channel speech separation, where the number of speakers is unknown, and each speaker may speak multiple utterances. We propose a speech separation model that simultaneously performs separation, dynamically estimates the number of speakers, and detects individual speaker activities by integrating an attractor module. The proposed system outperforms existing methods by introducing an attractor-based architecture that effectively combines local and global temporal modeling for multi-utterance scenarios. To evaluate the method in reverberant and noisy conditions, a multi-speaker multi-utterance dataset was synthesized by combining Librispeech speech signals with WHAM! noise signals. The results demonstrate that the proposed system accurately estimates the number of sources. The system effectively detects source activities and separates the corresponding utterances into correct outputs in both known and unknown source count scenarios. |
| title | Attractor-Based Speech Separation of Multiple Utterances by Unknown Number of Speakers |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2505.16607 |