Yunkeun Lee

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.

Human-robot interface using robust speech recognition and user localization based on noise separation device

This paper introduces a robust human-robot interface (HRI) system using a speech recognition and a user localization. For a robust speech recognition indoors under unknown noises and acoustic reverberations, a blind source separation (BSS) algorithm is implemented by a block-wise processing and developed using digital signal processing board to guarantee real-time operation. And a reverberation-robust sound source localization algorithm using separated signals is proposed. Although the BSS method cannot completely preserve the room acoustic information, the proposed localization algorithm overcomes this problem using target channel selection and target-emphasized enhancement methods. The developed algorithms are integrated into the commercial robot system to provide overall voice-enabled HRI. A series of tests are conducted to evaluate the performance of the BSS-based speech recognition and user localization method, and the results show a remarkable performance even under severe non-stationary noise conditions.

Domain-Adapted Word Segmentation for an Out-of-Domain Language Modeling

This paper introduces a domain-adapted word segmentation approach to text where a word delimiter is not used regularly. It depends on an unknown word extraction technique. This approach is essential for language modeling to adapt to new domains since a vocabulary set is activated in a word segmentation step. We have achieved ERR 21.22% in Korean word segmentation. In addition, we show that an incremental domain adaptation of the word segmentation decreases the perplexity of input text gradually. It means that our approach supports an out-of-domain language modeling.

Implementation of a Large-Scale Language Model in a Cloud Environment for Human–Robot Interaction

This paper presents a large-scale language model for daily-generated large-size text corpora using Hadoop in a cloud environment for improving the performance of a human–robot interaction system. Our large-scale trigram language model, consisting of 800 million trigram counts, was successfully implemented through a new approach using a representative cloud service (Amazon EC2), and a representative distributed processing framework (Hadoop). We performed trigram count extraction using Hadoop MapReduce to adapt our large-scale language model. Three hours are estimated on six servers to extract trigram counts for a large text corpus of 200 million word Twitter texts, which is the approximate number of daily-generated Twitter texts.

Design of tandem architecture using segmental trend features

This paper investigates the tandem architecture (TA) based on segmental features. The segmental feature based recognition system has been reported to show better results than the conventional feature based system in previous studies. In this paper we tried to merge the segmental feature with the tandem architecture which uses both hidden Markov models and neural networks. In general, segmental features can be separated into the trend and location. Since the trend means variation of segmental features and since it occupies a large portion of segmental features, the trend information was used as an independent or additional feature for the speech recognition system. We applied the trend information of segmental features to TA and used posterior probabilities, which are the output of the neural network, as inputs of the recognition system. Experiments were performed on Aurora2 database to examine the potentiality of the trend feature based TA. The results of our experiments verified that the proposed system outperforms the conventional system on very low SNR environments. These findings led us to conclude that the trend information on TA can be additionally used for the traditional MFCC features.