Learn More
There are two types of methods for tone recognition of continuous speech: one-step and two-step approaches. Two-step approaches need to identify the syllable boundaries firstly, while one-step approaches do not. Previous studies mostly focus on two-step approaches. In this paper, a one-step approach using Multi-space distribution HMM (MSD-HMM) is(More)
In this paper, a hybrid measure for automatic scoring of Mandarin pronunciation quality is presented. Different to prevalent algorithms, mono-phone-based and tri-phone-based acoustic models are applied and two types of features are combined to get the score of “Goodness of Pronunciations” with Support Vector Machine algorithm, which are the(More)
Acoustic confusions degrade the accuracy of pronunciation assessment severely in Computer Assisted Language Learning (CALL) systems. This paper presents our recent study on optimal modeling of the acoustic confusions. We change the traditional mandarin syllable structure, which is composed of initial and final, to a novel phoneme structure. Several phoneme(More)
This paper presents our recent study in resolving some specific acoustic problems of the computer assisted language learning (CALL) system by modifying the acoustic model (AM) and feature under ASR framework. Firstly, speaker dependent cepstrum mean normalization (Speaker CMN) is adopted to alleviate the distortion of channel, with which the average(More)
Pronunciation measure computation is a vital part of Computer Assisted Pronunciation Training (CAPT) system. This paper conducts some research on pronunciation measures based on the two popular measures - Log posterior probability (LPP) and Goodness of Pronunciation (GOP). A modified GOP - AGOP is proposed which directly uses the segmentation information of(More)
This paper presents our Mandarin pronunciation quality assessment system for the examination of Putonghua Shuiping Kaoshi (PSK) and investigates some measures to improve the assessment accuracy. In this paper, a selective speaker adaptation method is studied. In the adaptation module, we select well pronounced speech as the adaptation data, and adopt(More)
As the most effective confidence measure in computer assisted language learning system, the posterior probability is used widely, in which some tricks are applied to reduce the computation complexity. In this paper, we analysis the defect of the traditional algorithm and propose some improvements. Firstly, the traditional algorithm adopts the method of(More)
In this paper we present our investigation into improving the performance of our computer-assisted language learning (CALL) system through exploiting the acoustic model and features within the speech recognition framework. First, to alleviate channel distortion, speakerdependent cepstrum mean normalization (CMN) is adopted and the average correlation(More)