Mixing processThe target utterances are speech utterances from the Linguistic Data Consortium's CSR-I (WSJ0) dataset. As in the 2011 CHiME Challenge, each utterance has been convolved with a fixed Binaural Room Impulse Response (BRIR) corresponding to a frontal position at a distance of 2 m, then mixed with binaural recordings of genuine room noise made over a period of days in the same family living room. The temporal placement of the utterances within the noise background has been controlled in a manner which produces mixtures at 6 different ranges of SNR with limited rescaling of the speech and noise signals: -6, -3, 0, 3, 6, 9 dB.
Training, development and test data
The data are now available through the LDC as LDC2017S10.All data are provided as 16 bit stereo WAV files sampled at 16 kHz. The noisy utterances are provided both in isolated form and in embedded form. The latter involve 5 s of background noise before and after the utterance.
Training set: 7138 reverberated utterances from a total of 83 speakers forming the WSJ0 SI-84 training set and the same utterances each mixed at one random SNR.
Development set: 409 noisy utterances from 10 other speakers, forming the “no verbal punctuation” (NVP) part of the WSJ0 speaker-independent 5k vocabulary development set at each of 6 ranges of SNR.
Test set: 330 noisy utterances from 12 other speakers, forming the Nov’92 ARPA WSJ evaluation set (NVP, 5k vocabulary) at each of 6 ranges of SNR.
Different BRIRs are used for each dataset and these are the same as in the 2011 CHiME Challenge.
In addition to the above data, we also provide 7h of noise background and an optional set of noisy utterances with larger vocabulary, derived from the WSJ0 speaker-independent 20k vocabulary development and test sets at each of 6 ranges of SNR. These data are not part of the challenge, but you are welcome to use them provided that you also report the results obtained using the official training and development sets.
Public data subset
A "public" subset of the development test set is made available here to all participants for the purpose of evaluation only under agreement with the LDC. It consists of 240 stereo audio files which are noisy versions of 40 utterances by one female speaker and one male speaker of the WSJ0 development set (si_dt_05) at 6 different SNRs.
(For ftp downloads log in with user name 'anonymous')
If you eventually use these data in any published research please cite
- Vincent, E., Barker, J., Watanabe, S., Le Roux, J., Nesta, F. and Matassoni, M. The second CHiME Speech Separation and Recognition Challenge: Datasets, tasks and baselines'' In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, Vancouver
- Barker, J.P., Vincent, E., Ma, N., Christensen, H. and Green, P.D. "The PASCAL CHiME Speech Separation and Recognition Challenge", Computer Speech and Language, 27:3 (2013) pages 621-633
These tools include 3 baseline speaker-independent recognition systems trained on clean, reverberated and noisy data, respectively, and a number of scripts allowing you to
- train a baseline recognition system from the training data after processing by your own denoising front end (through retraining, not flat start),
- transcribe utterances in the development and test sets using one of the 3 provided systems or your own trained system,
- score the resulting transcriptions in terms of word error rate.
Which information can I use?
You are encouraged to use the embedded data in any way that may help, e.g. to learn about the acoustic environment in general, or the immediate acoustic context of each utterance. However, you should not train models of the noise background within a given test utterance on other test utterances. Because the noise signals in different utterances temporally overlap, this would lead to strong overfitting.
Which information shall I not use?The systems should not exploit:
- the SNR labels in the test data,
- the fact that the same utterances are used at each SNR,
- the fact that the same noise backgrounds are used in the development and test sets,
- the fact that the same utterances are used within the clean, reverberated and noisy training sets.
It is allowed to jointly process all the test utterances but the fact that the BRIRs are identical between different test utterances shall not be explicitly used.
All parameters should be tuned on the training set or the development set. Once you are satisfied with your system's tuning, run it only once on the final test set.