Total: 1
In this paper, we present an automatic accent analysis system that is based on phonological features (PFs). The proposed system exploits the knowledge of articulation embedded in phonology by rapidly build Markov models (MMs) of PFs extracted from accented speech. The Markov models capture information in the PF space along two dimensions of articulation: PF state-transitions and state-durations. Furthermore, by utilizing MMs of native and non-native accents a new statistical measure of accentedness is developed which rates the articulation of a word on a scale of native-like (-1) to non-native like (+1). The proposed methodology is then used to perform an automatic cross-sectional study of accented English spoken by native speakers of Mandarin Chinese (N-MC). The experimental results demonstrate the capability of the proposed system to rapidly perform quantitative as well as qualitative analysis of foreign accents. The work developed in this paper is easily assimilated into language learning systems, and has impact in the areas of speaker and speech recognition.