Show simple item record

dc.contributor.advisor Manamela, M. J. D.
dc.contributor.advisor Gasela, N. Ramaboka, Manthiba Elizabeth 2018-12-19T08:27:16Z 2018-12-19T08:27:16Z 2018
dc.description Thesis (M.Sc. (Computer Science)) --University of Limpopo, 2018 en_US
dc.description.abstract The conversion of speech to text is essential for communication between speech and visually impaired people. The focus of this study was to develop and evaluate an ASR baseline system designed for normal speech to correct speech disorders. Normal and disordered speech data were sourced from Lwazi project and UCLASS, respectively. The normal speech data was used to train the ASR system. Disordered speech was used to evaluate performance of the system. Features were extracted using the Mel-frequency cepstral coefficients (MFCCs) method in the processing stage. The cepstral mean combined variance normalization (CMVN) was applied to normalise the features. A third-order language model was trained using the SRI Language Modelling (SRILM) toolkit. A recognition accuracy of 65.58% was obtained. The refinement approach is then applied in the recognised utterance to remove the repetitions from stuttered speech. The approach showed that 86% of repeated words in stutter can be removed to yield an improved hypothesized text output. Further refinement of the post-processing module ASR is likely to achieve a near 100% correction of stuttering speech Keywords: Automatic speech recognition (ASR), speech disorder, stuttering en_US
dc.format.extent x, 49 leaves en_US
dc.language.iso en en_US
dc.relation.requires PDF en_US
dc.subject Speech recognition system en_US
dc.subject Speech disorders en_US
dc.subject.lcsh Automatic speech recognition en_US
dc.subject.lcsh Speech disorders -- Congresses en_US
dc.title Automatic speech recognition system for people with speech disorders en_US
dc.type Thesis en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ULSpace


My Account