The Australian National Database of Spoken Language
By J. Millar, Julie Vonwiller, J. Harrington, P. Dermody. 1994
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Arsha Nagrani
- Joon Son Chung
- Andrew Zisserman
- Weidi Xie
- Andrea Vedaldi
- Kristen Grauman
- Yoshua Bengio
- Samuel Albanie
Citations in the corpus
- VoxCeleb2: Deep Speaker Recognition (2018, background): “…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 speaker community [34, 33] (see…”
- 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 - ”
- The Acoustic Characteristics of /hVd/ Vowels in the Speech of some Australian Teenagers (2006, methodology): “The broadness categorization system has continued to be used to classify Australian speakers (Harrington et al. 1997; Horvath 1985; Millar et al. 1994) however its future usefulness is questionable in the light of suggestions that the numbers of speakers from the extremities of the Broadness…”
- 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) ”
- The Speakers in the Wild (SITW) Speaker Recognition Database (2016, background): “These databases are focused on constrained conditions such as controlled collection of clean microphone speech [1, 2], clean speech captured from a mobile device [3], and telephone speech from forensic cases [4].”