Fundamentals of speech recognition
Fundamentals of speech recognition
Speech recognition by machines and humans
Speech Communication
Using tone information in Cantonese continuous speech recognition
ACM Transactions on Asian Language Information Processing (TALIP)
Learning models for English speech recognition
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Automatic summarization of voicemail messages using lexical and prosodic features
ACM Transactions on Speech and Language Processing (TSLP)
Minimizing speaker variation effects for speaker-independent speech recognition
HLT '91 Proceedings of the workshop on Speech and Natural Language
Improving Speech Recognition and Understanding using Error-Corrective Reranking
ACM Transactions on Asian Language Information Processing (TALIP)
TRUES: Tone Recognition Using Extended Segments
ACM Transactions on Asian Language Information Processing (TALIP)
Efficient likelihood evaluation and dynamic Gaussian selection for HMM-based speech recognition
Computer Speech and Language
Continuous speech recognition with sparse coding
Computer Speech and Language
Speech recognition use in healthcare applications
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
Feature Compensation Techniques for ASR on Band-Limited Speech
IEEE Transactions on Audio, Speech, and Language Processing
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The accent with which words are spoken can have a strong effect on the performance of a speech recognition system. In a multilingual country such as South Africa where English is not the first language of most citizens, the need to address this issue is critical when building speech-based systems. In this project we trained two sets of hidden Markov Models for isolated word English speech. The first set of models was trained with native English speakers and the second set was trained with non-native speakers from a representative sample of major South African accent groups. We compared the recognition accuracies of the two sets of models and found that the models trained with accented English performed better. This preliminary research indicates that there is merit to committing resources to the task of accented training.