Automatic Accent Recognition Systems and the Effects of Data on Performance

This paper considers automatic accent recognition system performance in relation to the specific nature of the accent data. This is of relevance to the forensic application, where an accent recogniser may have a place in casework involving various accent classification tasks with different challenges attached. The study presented here is composed of two main parts. Firstly, it examines the performance of five different automatic accent recognition systems when distinguishing between geographically-proximate accents. Using geographically-proximate accents is expected to challenge the systems by increasing the degree of similarity between the varieties we are trying to distinguish between: a type of task which may be of use to forensic speech analysts. The second part of the study is concerned with identifying the specific phonemes which are important in a given accent recognition task, and eliminating those which are not. Depending on the varieties we are classifying, the phonemes which are most useful to the task will vary. This study therefore integrates feature selection methods into the accent recognition system shown to be the highest performer, the Y-ACCDIST-SVM system [1], to help to identify the most valuable speech segments and to increase accent recognition rates.