Accent in Speech Samples: Support Vector Machines for Classification and Rule Extraction

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Accent is the pattern of pronunciation which can identify a person’s linguistic, social or cultural background. It is an important source of inter-speaker variability and a particular problem for automated speech recognition. This study aims to investigate the effectiveness of rule extraction from support vector machines for speech accent classification. The presence of a speaker’s accent in the speech signal has significant implications for the accuracy of speech recognition because the effectiveness of an Automatic Speech Recognition System (ASR) is greatly reduced when the particular accent or dialect in the speech samples on which it is trained differs from the accent or dialect of the end-user [4] [14]. The correct identification of a speaker’s accent, and the subsequent use of the appropriately trained system, can be used to improve the efficiency and accuracy of the ASR application. If used in automated telephone helplines, analysing accent and then directing callers to the appropriately-accented response system may improve customer comfort and understanding. The increasing use of speech recognition technology in modern applications by people with a wide variety of linguistic and cultural backgrounds, means that addressing accent-related variability in speech is an important area of ongoing research. Rule extraction in this context can aid in the refinement of the design of a successful classifier, by discovering the contribution of the various input features, as well as by facilitating the comparison of the results with other machine learning methods.