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Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : University of Stellenbosch
2011
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| _version_ | 1867614050051948544 |
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| access_status_str | Open Access |
| author | Goussard, George Willem |
| author2 | Niesler, T. R. |
| author_browse | Goussard, George Willem Niesler, T. R. |
| author_facet | Niesler, T. R. Goussard, George Willem |
| author_sort | Goussard, George Willem |
| collection | Thesis |
| dc_rights_str_mv | University of Stellenbosch |
| description | Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/6686 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:45:52.267Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2011 |
| publishDateRange | 2011 |
| publishDateSort | 2011 |
| publisher | Stellenbosch : University of Stellenbosch |
| publisherStr | Stellenbosch : University of Stellenbosch |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/6686 Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems Goussard, George Willem Niesler, T. R. University of Stellenbosch. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Automatic speech recognition Pronunciation dictionary TIMIT Automatically determined phonetic baseforms Dissertations -- Electronic engineering Theses -- Electronic engineering Natural language processing (Computer science) Speech processing systems Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. ENGLISH ABSTRACT: This thesis presents a system that is designed to replace the manual process of generating a pronunciation dictionary for use in automatic speech recognition. The proposed system has several stages. The first stage segments the audio into what will be known as the subword units, using a frequency domain method. In the second stage, dynamic time warping is used to determine the similarity between the segments of each possible pair of these acoustic segments. These similarities are used to cluster similar acoustic segments into acoustic clusters. The final stage derives a pronunciation dictionary from the orthography of the training data and corresponding sequence of acoustic clusters. This process begins with an initial mapping between words and their sequence of clusters, established by Viterbi alignment with the orthographic transcription. The dictionary is refined iteratively by pruning redundant mappings, hidden Markov model estimation and Viterbi re-alignment in each iteration. This approach is evaluated experimentally by applying it to two subsets of the TIMIT corpus. It is found that, when test words are repeated often in the training material, the approach leads to a system whose accuracy is almost as good as one trained using the phonetic transcriptions. When test words are not repeated often in the training set, the proposed approach leads to better results than those achieved using the phonetic transcriptions, although the recognition is poor overall in this case. AFRIKAANSE OPSOMMING: Die doelwit van die tesis is om ’n stelsel te beskryf wat ontwerp is om die handgedrewe proses in die samestelling van ’n woordeboek, vir die gebruik in outomatiese spraakherkenningsstelsels, te vervang. Die voorgestelde stelsel bestaan uit ’n aantal stappe. Die eerste stap is die segmentering van die oudio in sogenaamde sub-woord eenhede deur gebruik te maak van ’n frekwensie gebied tegniek. Met die tweede stap word die dinamiese tydverplasingsalgoritme ingespan om die ooreenkoms tussen die segmente van elkeen van die moontlike pare van die akoestiese segmente bepaal. Die ooreenkomste word dan gebruik om die akoestiese segmente te groepeer in akoestiese groepe. Die laaste stap stel die woordeboek saam deur gebruik te maak van die ortografiese transkripsie van afrigtingsdata en die ooreenstemmende reeks akoestiese groepe. Die finale stap begin met ’n aanvanklike afbeelding vanaf woorde tot hul reeks groep identifiseerders, bewerkstellig deur Viterbi belyning en die ortografiese transkripsie. Die woordeboek word iteratief verfyn deur oortollige afbeeldings te snoei, verskuilde Markov modelle af te rig en deur Viterbi belyning te gebruik in elke iterasie. Die benadering is getoets deur dit eksperimenteel te evalueer op twee subversamelings data vanuit die TIMIT korpus. Daar is bevind dat, wanneer woorde herhaal word in die afrigtingsdata, die stelsel se benadering die akkuraatheid ewenaar van ’n stelsel wat met die fonetiese transkripsie afgerig is. As die woorde nie herhaal word in die afrigtingsdata nie, is die akkuraatheid van die stelsel se benadering beter as wanneer die stelsel afgerig word met die fonetiese transkripsie, alhoewel die akkuraatheid in die algemeen swak is. 2011-02-28T08:39:28Z 2011-03-14T08:31:22Z 2011-02-28T08:39:28Z 2011-03-14T08:31:22Z 2011-03 Thesis http://hdl.handle.net/10019.1/6686 en_ZA University of Stellenbosch 71 p. : ill. application/pdf Stellenbosch : University of Stellenbosch |
| spellingShingle | Automatic speech recognition Pronunciation dictionary TIMIT Automatically determined phonetic baseforms Dissertations -- Electronic engineering Theses -- Electronic engineering Natural language processing (Computer science) Speech processing systems Goussard, George Willem Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| title | Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| title_full | Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| title_fullStr | Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| title_full_unstemmed | Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| title_short | Unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| title_sort | unsupervised clustering of audio data for acoustic modelling in automatic speech recognition systems |
| topic | Automatic speech recognition Pronunciation dictionary TIMIT Automatically determined phonetic baseforms Dissertations -- Electronic engineering Theses -- Electronic engineering Natural language processing (Computer science) Speech processing systems |
| url | http://hdl.handle.net/10019.1/6686 |
| work_keys_str_mv | AT goussardgeorgewillem unsupervisedclusteringofaudiodataforacousticmodellinginautomaticspeechrecognitionsystems |