{"data":{"id":"10.15131/shef.data.4522925.v1","type":"dois","attributes":{"doi":"10.15131/shef.data.4522925.v1","prefix":"10.15131","suffix":"shef.data.4522925.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Ng, Wai Man","nameType":"Personal","givenName":"Wai Man","familyName":"Ng","affiliation":[],"nameIdentifiers":[]},{"name":"Kwan, Alvin C.M.","nameType":"Personal","givenName":"Alvin C.M.","familyName":"Kwan","affiliation":[],"nameIdentifiers":[]},{"name":"Lee, Tan","nameType":"Personal","givenName":"Tan","familyName":"Lee","affiliation":[],"nameIdentifiers":[]},{"name":"Hain, Thomas","nameType":"Personal","givenName":"Thomas","familyName":"Hain","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"ShefCE: A Cantonese-English bilingual speech corpus -- speech recognition model sets and recording transcripts"}],"publisher":"The University of Sheffield","container":{},"publicationYear":2017,"subjects":[{"subject":"200311 Chinese Languages","subjectScheme":"FOR"},{"subject":"FOS: Languages and literature","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Languages and literature","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"200303 English as a Second Language","subjectScheme":"FOR"},{"subject":"200302 English Language","subjectScheme":"FOR"},{"subject":"80107 Natural Language Processing","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2017-03-10","dateType":"Created"},{"date":"2021-04-13","dateType":"Updated"},{"date":"2017","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1109/icassp.2017.7953273","relatedIdentifierType":"DOI"},{"relationType":"IsIdenticalTo","relatedIdentifier":"10.15131/shef.data.4522925","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["126033816 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This online repository contains the speech recognition model sets and the recording transcripts used in the phoneme/syllable recognition experiments reported in [1].\u003cbr\u003eSpeech recognition model sets\u003cbr\u003e-----------------------------------------The speech recognition model sets are available as a tarball,named model.tar.gz, in this repository.\u003cbr\u003eThe 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.\u003cbr\u003e1. 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 speech3. 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)\u003cbr\u003eRecording transcripts\u003cbr\u003e----------------------------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.\u003cbr\u003eFour 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)\u003cbr\u003e\u003cbr\u003eThe ShefCE corpus data can be accessed online with DOI:10.15131/shef.data.4522907\u003cbr\u003ePlease cite [1] for the use of ShefCE data, models or transcripts.[1] Raymond W. M. Ng, Alvin C.M. Kwan, Tan Lee and Thomas Hain, \"ShefCE: A Cantonese-English Bilingual Speech Corpus for Pronunciation Assessment\", in \u003ci\u003eProc. The 42th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\u003c/i\u003e, 2017.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://figshare.shef.ac.uk/articles/dataset/ShefCE_A_Cantonese-English_bilingual_speech_corpus_--_speech_recognition_model_sets_and_recording_transcripts/4522925/1","contentUrl":[],"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1,"viewsOverTime":[{"yearMonth":"2025-10","total":1}],"downloadCount":0,"downloadsOverTime":[{"yearMonth":"2025-10","total":0}],"referenceCount":1,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-03-10T14:07:10.000Z","registered":"2017-03-10T14:07:10.000Z","published":"2017","updated":"2021-04-21T18:40:44.000Z"},"relationships":{"client":{"data":{"id":"bl.shef","type":"clients"}},"provider":{"data":{"id":"ooki","type":"providers"}},"media":{"data":{"id":"10.15131/shef.data.4522925.v1","type":"media"}},"references":{"data":[{"id":"10.1109/icassp.2017.7953273","type":"dois"}]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}