We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-field 7-microphone array devices positioned at different locations in the recording room. The dataset contains the audio recordings and human labeled transcripts of a total of 10 sessions with a duration between 15 and 45 minutes. The corpus was created to advance in the field of noise robust and distant speech processing and is intended to serve as a public research and benchmarking data set.
This noisy speech test set is created from the Google Speech Commands v2 [1] and the Musan dataset[2]. It could be downloaded here: https://zenodo.org/record/6066174#.Yn7NPJPMLyU Specifically, we created this test set by mixing the speech in the Google Speech Commands v2 test set with random noise in the Musan dataset at different signal to noise ratio -12.5,-10,0,10,20,30 and 40 decibel (dB). The Google Speech Commands v2 dataset is under the Creative Commons BY 4.0 license. It could be downloaded at: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz The Musan dataset is under Attribution 4.0 International (CC BY 4.0). It could be downlowned at https://www.openslr.org/17/ Citations: [1] Pete Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,” arXiv preprint arXiv:1804.03209, 2018. [2] David Snyder, Guoguo Chen, and Daniel Povey, “Musan: A music, speech, and noise corpus,” arXiv preprint arXiv:1510.08484, 2015.
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)
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