ShefCE: A Cantonese-English bilingual speech corpus -- speech recognition model sets and recording transcripts

Speech recognition model sets

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The speech recognition model sets are available as a tarball,

named model.tar.gz, in this repository.

The models were trained on Cantonese and English data. For each language, two model sets were trained according to the background setting and the mixed-condition setting respectively. All models are DNN-HMM models, which are hybrid feed-forward neural network models with 6 hidden layers and 2048 neurons per layer. Details can be found in [1]. The Cantonese models include a bigram syllable language model. The English models include a bigram phoneme language model. All model sets are provided in the kaldi format.

1. The background-cantonese model was trained on CUSENT (68 speakers, 19.4 hours) of read Cantonese speech.

2. The background-english model was trained on WSJ-SI84 (83 speakers, 15.2 hours) of read English speech

3. The mixed-condition-cantonese model was trained on background-cantonese data and ShefCE Cantonese training data (25 speakers, 9.7 hours).

4. The mixed-condition-english model was trained on background-english data and ShefCE English training data (25 speakers, 2.3 hours)

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The recording transcripts are available as a tarball, named, stms.tar.gz, in this repository. These transcripts cover the ShefCE portion of the training data and the ShefCE test data.

Four files can be found in the stms.tar.gz archive. 

- ShefCE_RC.train.v*.stm contains the transcripts for ShefCE training set (Cantonese)

- ShefCE_RE.train.v*.stm contains the transcripts for ShefCE training set (English)

- ShefCE_RC.test.v*.stm contains the transcripts for ShefCE test set (Cantonese)

- ShefCE_RE.test.v*.stm contains the transcripts for ShefCE test set (English)

The ShefCE corpus data can be accessed online with DOI:10.15131/shef.data.4522907

Please cite [1] for the use of ShefCE data, models or transcripts.