The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Speaker identification on the SCOTUS corpus
Abstract
This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near-100% text-independent identification accuracy on utterances that are longer than one second; (2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; (3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved “perfect” forced alignment on very long speech segments (one hour). These findings and their significance are discussed.
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Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
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Metadata
- Authors
- Yuan, J., & Liberman, M.
- Venue
- The Journal of the Acoustical Society of America
- Publisher
- Acoustical Society of America (ASA)
- Published
- 2008-05-01
- Volume
- 123
- Issue
- 5_Supplement
- Pages
- 3878-3878
- ISSN
- 0001-4966 (ISSN-L)
- 1520-8524
- OpenAlex
- W2029996593
- Cited by
- 594
- References
- 1
- Type
- conference abstract
- Language
- en
- Open access
- closed
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