Attribute-based Mandarin speech recognition using conditional random fields

Integrating phonetic knowledge into a speech recognizer is a possible way to further improve the performance of conventional HMM-based speech recognition methods. This paper presents a cascaded architecture which consists of attribute detection and conditional random field to make use of phonetic knowledge within the phone decoding process. The attribute detection can be implemented by using any effective feature extraction approaches. In this study, an HMM-based method is applied for attribute tagging of Mandarin speech. Then a conditional random field method which applies attribute labels as the input vectors is used to perform the speech recognition. The preliminary experiment result shows that the proposed method is very promising and worthy for further investigation.