Contact
dpovey@gmail.com
Phone: 425 247 4129
(Daniel Povey)

Chime 6 Models

This resource contains pretrained models for the Chime 6 challenge, including models for the baseline and the JHU-CLSP submission. There is a separate package for the speech activity detection (SAD), speaker diarization, and automatic speech recognition (ASR) components. Contact Chime 6 organizer Shinji Watanabe (email: shinjiw@ieee.org) for questions about this resource.

Chime 6 SAD system

Date
2019-11-18
Uploader
Desh Raj
Recipe
None
Model Type
Speech Activity Detection (SAD), TDNN + Stats pooling
Error Rate
5.1% error rate on Chime 6 dev
Notes
Trained on the Chime 6 training data

Chime 6 Baseline Diarization system

Date
2019-11-18
Uploader
David Snyder
Recipe
None
Model Type
Diarization, x-vector
Error Rate
61.6% DER on Chime 6 dev (using baseline SAD)
Notes
Extractor trained on reverberated Voxceleb, backend trained on Chime 6 training data

Chime 6 Baseline ASR system

Date
2019-11-18
Uploader
Ashish Arora
Recipe
None
Model Type
ASR, TDNN-F, chain
Error Rate
51.8% WER on Chime 6 dev (track 1 conditions)
Notes
Trained on Chime 6 training data with augmentations

JHU-CLSP Diarization system

Date
2021-04-14
Uploader
Desh Raj
Recipe
None
Model Type
Diarization, x-vector, i-vector, VB resegmentation
Error Rate
51.0% DER on Chime 6 dev (using baseline SAD)
Notes
Same x-vector extractor as baseline, i-vector extractor for VB resegmentation trained on challenge data (see stage -1 in s5b_track2/run.sh)

JHU-CLSP ASR system

Date
2019-04-14
Uploader
Ashish Arora
Recipe
None
Model Type
ASR, TDNN-F, chain, RNNLM
Error Rate
43.3% WER on Chime 6 dev (track 1 conditions)
Notes
CNN-TDNNF model trained with augmentations and RNNLM rescoring