POLYCOST: A telephone-speech database for speaker recognition

By J. Hennebert, H. Melin, D. Petrovska-Delacrétaz, D. Genoud. 2000

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VoxCeleb2: Deep Speaker Recognition (2018, background): “…suffer from one or more of the following limitations: (i) they are ei-a ther obtained under controlled conditions (e.g., from telephone calls [29, 30] or acoustic laboratories [31, 32, 33]), (ii) they are manually annotated and hence limited in size [8], or (iii) not freely available to the…”
VoxCeleb: A Large-Scale Speaker Identification Dataset (2017, background): “ELSDSR [26] Clean Speech 22 198 MIT Mobile [21] Mobile Devices - 88 7,884 SWB [27] Telephony - 3,114 33,039 POLYCOST [17] Telephony - 133 1,285 ‡ ICSI Meeting Corpus [23] Meetings - 53 922 Forensic Comparison [22] Telephony 552 1,264 ANDOSL [18] Clean speech - 204 33,900 TIMIT [28] † Clean speech - ”
Voxceleb: Large-scale speaker verification in the wild (2020, background): “The statistics for all the (Godfrey et al.,1992) Telephony (cid:1) 3114 33,039 POLYCOST (Hennebert et al., 2000) Telephony (cid:1) 133 1285 z ICSI Meeting Corpus (Janin et al., 2003) Meetings (cid:1) 53 922 Forensic Comparison (Morrison et al., 2015) Telephony @ 552 1264 ANDOSL (Millar et al.,1994) ”