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Deep Learning for Automatic Assessment and Feedback of Spoken English

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cam.depositDate2022-03-12
cam.restrictionthesis_access_open
cam.supervisorGales, Mark JF
cam.supervisor.orcidGales, Mark [0000-0002-5311-8219]
dc.contributor.authorKyriakopoulos, Konstantinos
dc.contributor.orcidKyriakopoulos, Konstantinos [0000-0002-7659-4541]
dc.date.accessioned2022-03-30T13:42:43Z
dc.date.available2022-03-30T13:42:43Z
dc.date.submitted2021-10
dc.date.updated2022-03-12T09:52:51Z
dc.description.abstractGrowing global demand for learning a second language (L2), particularly English, has led to considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications. This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One of the challenges in automatic spoken language assessment is giving candidates feedback on particular aspects, or views, of their spoken language proficiency, in addition to the overall holistic score normally provided. Another is detecting pronunciation and other types of errors at the word or utterance level and feeding them back to the learner in a useful way. It is usually difficult to obtain accurate training data with separate scores for different views and, as examiners are often trained to give holistic grades, single-view scores can suffer issues of consistency. Conversely, holistic scores are available for various standard assessment tasks such as Linguaskill. An investigation is thus conducted into whether assessment scores linked to particular views of the speaker’s ability can be obtained from systems trained using only holistic scores. End-to-end neural systems are designed with structures and forms of input tuned to single views, specifically each of pronunciation, rhythm, intonation and text. By training each system on large quantities of candidate data, individual-view information should be possible to extract. The relationships between the predictions of each system are evaluated to examine whether they are, in fact, extracting different information about the speaker. Three methods of combining the systems to predict holistic score are investigated, namely averaging their predictions and concatenating and attending over their intermediate representations. The combined graders are compared to each other and to baseline approaches. The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. An approach to these tasks is presented by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora x of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level.
dc.identifier.doi10.17863/CAM.82947
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335514
dc.language.isoeng
dc.publisher.collegeQueens
dc.publisher.institutionUniversity of Cambridge
dc.rightsAll Rights Reserved
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectdeep learning
dc.subjectCALL
dc.subjectautomatic assessment
dc.subjectpronunciation assessment
dc.subjectrhythm assessment
dc.subjectprosody assessment
dc.subjecttext assessment
dc.subjectspontaneous speech
dc.subjectattention mechanisms
dc.titleDeep Learning for Automatic Assessment and Feedback of Spoken English
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
pubs.licence-identifierapollo-deposit-licence-2-1
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved/
rioxxterms.typeThesis

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