Multiple Pronunciations Modeling of Speaker Independent, Continuous Speech Recognition for Afaan Oromoo
| dc.contributor.advisor | H/Mariam, Sebsibe (PhD) | |
| dc.contributor.advisor | Tesfaye, Wondewosen (PhD) | |
| dc.contributor.author | Amberas, Onesmos | |
| dc.date.accessioned | 2021-03-17T09:29:32Z | |
| dc.date.accessioned | 2023-11-08T04:34:20Z | |
| dc.date.available | 2021-03-17T09:29:32Z | |
| dc.date.available | 2023-11-08T04:34:20Z | |
| dc.date.issued | 2013-01 | |
| dc.description.abstract | Automatic speech recognition, especially speaker independent continuous speech recognition, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on speaker independent speech recognition. One approach to handling this variation consists of expanding the dictionary with phonetic substitution, insertion, and deletion rules. This study aimed at modeling multiple pronunciations in speaker independent continuous speech recognition for Afaan Oromoo to handle pronunciation variation. Hidden Markov Model and the Hidden Markov Modeling Toolkit were used to implement it. For developing model for the language under study, a corpus containing 754 sentences collected from Bariisaa news paper and Afaan Oromoo bible (New Testament) was used. The data collected was preprocessed in line with the requirements of HTK. Phonemes were taken as base unit for recognition. Knowledge based pronunciation variation modeling technique was used for modeling words with multiple pronunciations. Thus two models were developed; one with canonical pronunciation and the other with alternate pronunciation to compare their relative performance. Accordingly, the performance achieved using canonical pronunciation was 81.09% and 83.82 % correct for sentences and words respectively with word accuracy of 80.91 % while the performance of alternate pronunciation was 83 .08% and 85. 11% sentences and words correct respectively with 82.52% word accuracy. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/25565 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Modeling | en_US |
| dc.subject | Speaker | en_US |
| dc.title | Multiple Pronunciations Modeling of Speaker Independent, Continuous Speech Recognition for Afaan Oromoo | en_US |
| dc.type | Thesis | en_US |