An Investigation of Phonological Feature Systems Used in Detection-Based ASR

Abstract

In this paper, we study the effect of using different phonological feature sets for detection-based automatic speech recognition in phone recognition tasks. Three phonological feature sets derived from different underlying phonological theories are investigated. Our experiments were conducted on the TIMIT database. By comparing the oracle phone recognition results achieved by assuming that all the phonological features are correctly detected based on each feature set, we show that selecting an appropriate phonological feature set is crucial to the performance of detection-based ASR. The highly accurate oracle phone recognition results show that the performance of the CRF-based backend, which is commonly used in detection-based ASR, is very satisfactory. Comparison of the oracle phone recognition results and the real phone recognition results indicates that investigation of high-accuracy front-end detectors is a key issue in improving the performance of detection-based ASR.