Multiple Choice Learning for Efficient Speech Separation with Many Speakers
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915037496475648 |
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| author | Perera, David Derrida, François Mariotte, Théo Richard, Gaël Essid, Slim |
| author_facet | Perera, David Derrida, François Mariotte, Théo Richard, Gaël Essid, Slim |
| contents | Training speech separation models in the supervised setting raises a permutation problem: finding the best assignation between the model predictions and the ground truth separated signals. This inherently ambiguous task is customarily solved using Permutation Invariant Training (PIT). In this article, we instead consider using the Multiple Choice Learning (MCL) framework, which was originally introduced to tackle ambiguous tasks. We demonstrate experimentally on the popular WSJ0-mix and LibriMix benchmarks that MCL matches the performances of PIT, while being computationally advantageous. This opens the door to a promising research direction, as MCL can be naturally extended to handle a variable number of speakers, or to tackle speech separation in the unsupervised setting. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_18497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiple Choice Learning for Efficient Speech Separation with Many Speakers Perera, David Derrida, François Mariotte, Théo Richard, Gaël Essid, Slim Sound Machine Learning Audio and Speech Processing Training speech separation models in the supervised setting raises a permutation problem: finding the best assignation between the model predictions and the ground truth separated signals. This inherently ambiguous task is customarily solved using Permutation Invariant Training (PIT). In this article, we instead consider using the Multiple Choice Learning (MCL) framework, which was originally introduced to tackle ambiguous tasks. We demonstrate experimentally on the popular WSJ0-mix and LibriMix benchmarks that MCL matches the performances of PIT, while being computationally advantageous. This opens the door to a promising research direction, as MCL can be naturally extended to handle a variable number of speakers, or to tackle speech separation in the unsupervised setting. |
| title | Multiple Choice Learning for Efficient Speech Separation with Many Speakers |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2411.18497 |