Speaker Identification

VoxCeleb1 is an audio dataset containing over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube.

CSI is a criminal conversational dataset for speaker identification built from the CSI television show. The authors collected transcripts of 39 episodes and video/audio of 4 episodes. Each episode involves on average more than 30 speakers. Utterances last on average 3 to 4 seconds. There are around 45 to 50 distinct scenes/conversations per episode.

The EVI dataset is a challenging, multilingual spoken-dialogue dataset with 5,506 dialogues in English, Polish, and French. The dataset can be used to develop and benchmark conversational systems for user authentication tasks, i.e. speaker enrolment (E), speaker verification (V), speaker identification (I). The dataset contains the audio data, machine transcriptions, and target identity for each dialogue, and the knowledge-base with personal information (postcode, name, and date of birth) for each identity. The dataset can be used with both text-independent biometric or knowledge-based authentication (KBA) tasks.

The Fearless Steps Initiative by UTDallas-CRSS led to the digitization, recovery, and diarization of 19,000 hours of original analog audio data, as well as the development of algorithms to extract meaningful information from this multichannel naturalistic data resource. As an initial step to motivate a stream-lined and collaborative effort from the speech and language community, UTDallas-CRSS is hosting a series of progressively complex tasks to promote advanced research on naturalistic “Big Data” corpora. This began with ISCA INTERSPEECH-2019: "The FEARLESS STEPS Challenge: Massive Naturalistic Audio (FS-#1)". This first edition of this challenge encouraged the development of core unsupervised/semi-supervised speech and language systems for single-channel data with low resource availability, serving as the “First Step” towards extracting high-level information from such massive unlabeled corpora. As a natural progression following the successful Inaugural Challenge FS#1, the FEARLESS STEPS Challenge Phase-#2 focuses on the development of single-channel supervised learning strategies. This FS#2 provides 80 hours of ground-truth data through Training and Development sets, with an additional 20 hours of blind-set Evaluation data. Based on feedback from the Fearless Steps participants, additional Tracks for streamlined speech recognition and speaker diarization have been included in the FS#2. The results for this Challenge will be presented at the ISCA INTERSPEECH-2020 Special Session. We encourage participants to explore any and all research tasks of interest with the Fearless Steps Corpus – with suggested Task Domains listed below. Research participants can, however, also utilize the FS#2 corpus to explore additional problems dealing with naturalistic data, which we welcome as part of the special session. --- This (FS-02) edition of the FEARLESS STEPS Challenge includes the following 6 tasks --- ## TASK 1: Speech Activity Detection (SAD) ## TASK 2: Speaker Identification (using Speaker Segments) (SID) ## TASK 3: Speaker Diarization ## ├── (3.a.) Track 1: Diarization using system SAD (SD_track1) ## └── (3.b.) Track 2: Diarization using reference SAD (SD_track2) ## TASK 4: Automatic Speech Recognition ## ├── (4.a.) Track 1: ASR using system Diarization/SAD (ASR_track1) ## └── (4.b.) Track 2: ASR using Diarized Segments (ASR_track2)