Automatic analysis of Mandarin accented English using phonological features

The problem of accent analysis and modeling has been considered from a variety of domains, including linguistic structure, statistical analysis of speech production features, and HMM/GMM (hidden Markov model/Gaussian mixture model) model classification. These studies however fail to connect speech production from a temporal perspective through a final classification strategy. Here, a novel accent analysis system and methodology which exploits the power of phonological features (PFs) is presented. The proposed system exploits the knowledge of articulation embedded in phonology by building 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 by a speaker 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 perform quantitative as well as qualitative analysis of foreign accents. The work developed in this study can be easily expanded into language learning systems, and has potential impact in the areas of speaker recognition and ASR (automatic speech recognition).

► This study presents a new model for accent based on phonological features (PFs).
► Pronunciation variations are viewed as different paths in the PFs space-time.
► Markov models learn the pronunciations variations between native and non-native speakers.
► Native speakers of Mandarin and American English are used for evaluation.
► Proposed system shows high correlation with human judgment of accent (0.89 with p < 0.01).