Detection-based accented speech recognition using articulatory features

We propose an attribute-based approach to accented speech recognition based on automatic speech attribute transcription with high efficiency detection of articulatory features. In order to utilize appropriate and extensible phonetic and linguistic knowledge, conditional random field (CRF) is designed to take frame-level inputs with binary feature functions. The use of CRF with merely the state features to generate probabilistic phone lattices is then utilized to solve the phone under-generation problem. Finally an attribute discrimination module is incorporated to handle a diversity of accent changes without retraining any model, leading to flexible “plug `n´ play” modular design. The effectiveness of the proposed approach is evaluated on three typical Chinese accents, namely Guanhua, Yue and Wu. Our method yields a significant absolute phone recognition accuracy improvement 5.04%, 4.68% and 6.06% for the corresponding three accent types over a conventional monophone HMM system. Compared to a context-dependent triphone HMM system, we achieve comparable phone accuracies at only less than 20% of the computation cost. In addition, our proposed method is equally applicable to speaker-independent systems handling multiple accents.