Byung Ok Kang

GenieTutor: A Computer-Assisted Second-Language Learning System Based on Spoken Language Understanding

This paper introduces a computer-assisted second-language learning system using spoken language understanding. The system consists of automatic speech recognition, semantic/grammar correction evaluation, and tutoring module. The speech recognition is optimized for non-natives as well as natives for educational purpose and smooth interaction. Semantic/grammar correction evaluation evaluates whether the non-native learners utterance is appropriate semantically and is correct grammatically. Tutoring module decides to go to the next turn or ask the learner to try again, and also provides a turn-by-turn corrective feedback using evaluation results. We constructed English learning service consisting of three stages such as Pronunciation Clinic, Think&Talk and Look&Talk using the system.

A useful feature-engineering approach for a LVCSR system based on CD-DNN-HMM algorithm

In this paper, we propose a useful feature-engineering approach for Context-Dependent Deep-Neural-Network Hidden-Markov-Model (CD-DNN-HMM) based Large-Vocabulary-Continuous-Speech-Recognition (LVCSR) systems. The speech recognition performance of a LVCSR system is improved from two feature-engineering perspectives. The first performance improvement is achieved by adopting the intra/inter-frame feature subsets when the Gaussian-Mixture-Model (GMM) HMMs for the HMM state-level alignment are built. And the second performance gain is then followed with the additional features augmenting the front-end of the DNN. We evaluate the effectiveness of our feature-engineering approach under a series of Korean speech recognition tasks (isolated single-syllable recognition with a medium-sized speech corpus and conversational speech recognition with a large-sized database) using the Kaldi speech recognition toolkit. The results show that the proposed feature-engineering approach outperforms the traditional Mel Frequency Cepstral Coefficient (MFCCs) GMM + Mel-frequency filter-bank output DNN method.