--- _id: '26770' abstract: - lang: eng text: "Automatic transcription of meetings requires handling of overlapped speech, which calls for continuous speech separation (CSS) systems. The uPIT criterion was proposed for utterance-level separation with neural networks and introduces the constraint that the total number of speakers must not exceed the number of output channels. When processing meeting-like data in a segment-wise manner, i.e., by separating overlapping segments independently and stitching adjacent segments to continuous output streams, this constraint has to be fulfilled for any segment. In this contribution, we show that this constraint can be significantly relaxed. We propose a novel graph-based PIT criterion, which casts the assignment of utterances to output channels in a graph coloring problem. It only requires that the number of concurrently active speakers must not exceed the number of output channels. As a consequence, the system can process an arbitrary number of speakers and arbitrarily long segments and thus can handle more diverse scenarios.\r\nFurther, the stitching algorithm for obtaining a consistent output order in neighboring segments is of less importance and can even be eliminated completely, not the least reducing the computational effort. Experiments on meeting-style WSJ data show improvements in recognition performance over using the uPIT criterion. " author: - first_name: Thilo full_name: von Neumann, Thilo id: '49870' last_name: von Neumann orcid: https://orcid.org/0000-0002-7717-8670 - first_name: Keisuke full_name: Kinoshita, Keisuke last_name: Kinoshita - first_name: Christoph full_name: Boeddeker, Christoph id: '40767' last_name: Boeddeker - first_name: Marc full_name: Delcroix, Marc last_name: Delcroix - first_name: Reinhold full_name: Haeb-Umbach, Reinhold id: '242' last_name: Haeb-Umbach citation: ama: 'von Neumann T, Kinoshita K, Boeddeker C, Delcroix M, Haeb-Umbach R. Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers. In: Interspeech 2021. ; 2021. doi:10.21437/interspeech.2021-1177' apa: 'von Neumann, T., Kinoshita, K., Boeddeker, C., Delcroix, M., & Haeb-Umbach, R. (2021). Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers. Interspeech 2021. Interspeech. https://doi.org/10.21437/interspeech.2021-1177' bibtex: '@inproceedings{von Neumann_Kinoshita_Boeddeker_Delcroix_Haeb-Umbach_2021, title={Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers}, DOI={10.21437/interspeech.2021-1177}, booktitle={Interspeech 2021}, author={von Neumann, Thilo and Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}, year={2021} }' chicago: 'Neumann, Thilo von, Keisuke Kinoshita, Christoph Boeddeker, Marc Delcroix, and Reinhold Haeb-Umbach. “Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers.” In Interspeech 2021, 2021. https://doi.org/10.21437/interspeech.2021-1177.' ieee: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers,” presented at the Interspeech, 2021, doi: 10.21437/interspeech.2021-1177.' mla: 'von Neumann, Thilo, et al. “Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers.” Interspeech 2021, 2021, doi:10.21437/interspeech.2021-1177.' short: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach, in: Interspeech 2021, 2021.' conference: name: Interspeech date_created: 2021-10-25T08:50:01Z date_updated: 2023-11-15T12:14:40Z ddc: - '000' department: - _id: '54' doi: 10.21437/interspeech.2021-1177 file: - access_level: open_access content_type: video/mp4 creator: tvn date_created: 2021-12-06T10:39:13Z date_updated: 2021-12-06T10:48:30Z file_id: '28327' file_name: Interspeech 2021 voiceover-002-compressed.mp4 file_size: 9550220 relation: supplementary_material title: Video for INTERSPEECH 2021 - access_level: open_access content_type: application/vnd.openxmlformats-officedocument.presentationml.presentation creator: tvn date_created: 2021-12-06T10:47:01Z date_updated: 2021-12-06T10:47:01Z file_id: '28328' file_name: Graph-PIT-poster-presentation.pptx file_size: 1337297 relation: slides title: Slides from INTERSPEECH 2021 - access_level: open_access content_type: application/pdf creator: tvn date_created: 2021-12-06T10:48:21Z date_updated: 2021-12-06T10:48:21Z file_id: '28329' file_name: INTERSPEECH2021_Graph_PIT.pdf file_size: 226589 relation: main_file file_date_updated: 2021-12-06T10:48:30Z has_accepted_license: '1' keyword: - Continuous speech separation - automatic speech recognition - overlapped speech - permutation invariant training language: - iso: eng oa: '1' project: - _id: '52' name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing' publication: Interspeech 2021 publication_status: published quality_controlled: '1' related_material: link: - relation: software url: https://github.com/fgnt/graph_pit status: public title: 'Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation of Arbitrary Numbers of Speakers' type: conference user_id: '49870' year: '2021' ...