2000 NIST Speaker Recognition Evaluation
|Item Name:||2000 NIST Speaker Recognition Evaluation|
|Author(s):||Mark Przybocki, Alvin Martin|
|LDC Catalog No.:||LDC2001S97|
|Data Source(s):||telephone speech|
|Application(s):||speaker verification, speaker segmentation and tracking, speaker identification, speech recognition|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2001S97 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Przybocki, Mark, and Alvin Martin. 2000 NIST Speaker Recognition Evaluation LDC2001S97. Web Download. Philadelphia: Linguistic Data Consortium, 2001.|
This publication contains the 2000 NIST Speaker Recognition Evaluation Corpus, Linguistic Data Consortium (LDC) catalog number LDC2001S97 and ISBN 1-58563-192-2. The 2000 NIST Speaker Recognition Evaluation is part of an ongoing series of yearly evaluations conducted by NIST. These evaluations provide an important contribution to the direction of research efforts and the calibration of technical capabilities. They are intended to be of interest to all researchers working on the general problem of text independent speaker recognition. To this end, the evaluation was designed to be simple, to focus on core technology issues, to be fully supported, and to be accessible.
This publication consists of 10,328 single channel SPHERE files encoded in 8-bit mulaw containing a total of approximately 4.31 Gbytes of data covering 148.9 hours of conversational telephone speech collected by LDC.
Supporting documentation for this evaluation may be found on the 2000 NIST Speaker Recognition Evaluation website.
Please note that there was an optional additional corpus in the original Evaluation. If you are interested in this AHUMADA corpus, please contact Javier Ortega-Garcia of the Universidad Politecnica de Madrid. Information on how to contact Dr. Ortega-Garcia is available at 2000 NIST Resources.
As of June, 27, 2017, 1,426 files that were left off were added back in. New downloads after this date will have the complete data set.