Publications

In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted a leaderboard style speaker recognition challenge using conversational telephone speech (CTS) data extracted from the unexposed portion of the Call My Net 2 (CMN2) corpus previously used in the 2018 Speaker Recognition Evaluation (SRE). The SRE19 CTS Challenge was organized in a similar manner to SRE18, except it offered only the open training condition. In addition, similar to the NIST i-vector challenge, the evaluation set consisted of two subsets: a progress subset, and a test subset. The progress subset comprised 30% of the trials and was used to monitor progress on the leaderboad, while the remaining 70% of the trials formed the test subset, which was used to generate the official final results determined at the end of the challenge. Which subset (i.e., progress or test) a trial belonged to was unknown to challenge participants, and each system submission had to contain outputs for all of trials. The CTS Challenge also served as a prerequisite for entrance to the main SRE19 whose primary task was audio-visual person recognition. A total of 67 organizations (forming 51 teams) from academia and industry participated in the CTS Challenge and submitted 1347 valid system outputs. This paper presents an overview of the evaluation and several analyses of system performance for all primary conditions in the CTS Challenge. Compared to the CTS track of the SRE18, the SRE19 CTS Challenge results indicate remarkable improvements in performance which are mainly attributed to 1) the availability of large amounts of in-domain development data from a large number of labeled speakers, 2) speaker representations (aka embeddings) extracted using extended and more complex end-to-end neural network frameworks, and 3) effective use of the provided large development set.

In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted the most recent in an ongoing series of speaker recognition evaluations (SRE). There were two components to SRE19: 1) a leaderboard style Challenge using unexposed conversational telephone speech (CTS) data from the Call My Net 2 (CMN2) corpus, and 2) an Audio-Visual (AV) evaluation using video material extracted from the unexposed portions of the Video Annotation for Speech Technologies (VAST) corpus. This paper presents an overview of the Audio-Visual SRE19 activity including the task, the performance metric, data, and the evaluation protocol, results and system performance analyses. The Audio-Visual SRE19 was organized in a similar manner to the audio from video (AfV) track in SRE18, except it offered only the open training condition. In addition, instead of extracting and releasing only the AfV data, unexposed multimedia data from the VAST corpus was used to support the Audio-Visual SRE19. It featured two core evaluation tracks, namely audio only and audio-visual, as well as an optional visual only track. A total of 26 organizations (forming 14 teams) from academia and industry participated in the Audio-Visual SRE19 and submitted 102 valid system outputs. Evaluation results indicate: 1) notable performance improvements for the audio only speaker recognition task on the challenging amateur online video domain due to the use of more complex neural network architectures (e.g., ResNet) along with soft margin losses, 2) state-of-the-art speaker and face recognition technologies provide comparable person recognition performance on the amateur online video domain, and 3) audio-visual fusion results in remarkable performance gains (greater than 85% relative) over the audio only or visual only systems.