• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.735-742
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• 2007

The importance of the interaction capability of a robot increases as the application of a robot is extended to a human's daily life. In this paper, a portable mediate interface Handybot is developed with various interaction channels to be used with an intelligent home service robot. The Handybot has a task-oriented channel of an icon language as well as a verbal interface. It also has an emotional interaction channel that recognizes a user's emotional state from facial expression and speech, transmits that state to the robot, and expresses the robot's emotional state to the user. It is expected that the Handybot will reduce spatial problems that may exist in human-robot interactions, propose a new interaction method, and help creating rich and continuous interactions between human users and robots.

• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.106-109
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• 1997

For the practical isolated word recognition system, the ability to reject the out-of -vocabulary(OOV) is required. In this paper, we present a rejection method which uses the clustered phoneme modeling combined with postprocessing by likelihood ratio scoring. Our baseline speech recognition system was based on the whole-word continuous HMM. And 6 clustered phoneme models were generated using statistical method from the 45 context independent phoneme models, which were trained using the phonetically balanced speech database. The test of the rejection performance for speaker independent isolated words recogntion task on the 22 section names shows that our method is superior to the conventional postprocessing method, performing the rejection according to the likelihood difference between the first and second candidates. Furthermore, this clustered phoneme models do not require retraining for the other isolated word recognition system with different vocabulary sets.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.298-303
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• 2023

This study aimed to investigate the clinical utility of voiced sentence tasks for voice evaluation. To this end, we analyzed the correlation between perturbation-based acoustic measurements [jitter percent (jitter), shimmer percent (shimmer), Noise to Harmonic Ratio (NHR)] using sustained vowel phonation, and cepstrum-based acoustic measurements [Cepstral Peak Prominence (CPP), Low/High spectral ratio (L/H ratio)] using voiced sentences. As a result of analyzing data collected from 65 patients with voice disorders, there was a significant correlation between the CPP and jitter (r = -.624, p = .000), shimmer (r = -.530, p = .000), NHR (r = -.469, p = .000).This suggests that the cepstrum measurement of voiced sentences can be used as an alternative to the analysis limitations of the pathological voice such as not possible perturbation-based acoustic measurement, and result difference according to the analysis section.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.199-210
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• 2002

In this paper, we carried out the study on speech recognition using the KM-Net topology design algorithm based on decision tree state-clustering to improve the performance of acoustic models in speech recognition. The Korean has many allophonic and grammatical rules compared to other languages, so we investigate the allophonic variations, which defined the Korean phonetics, and construct the phoneme question set for phonetic decision tree. The basic idea of the HM-Net topology design algorithm is that it has the basic structure of SSS (Successive State Splitting) algorithm and split again the states of the context-dependent acoustic models pre-constructed. That is, it have generated. the phonetic decision tree using the phoneme question sets each the state of models, and have iteratively trained the state sequence of the context-dependent acoustic models using the PDT-SSS (Phonetic Decision Tree-based SSS) algorithm. To verify the effectiveness of the above algorithm we carried out the speech recognition experiments for 452 words of center for Korean language Engineering (KLE452) and 200 sentences of air flight reservation task (YNU200). Experimental results show that the recognition accuracy has progressively improved according to the number of states variations after perform the splitting of states in the phoneme, word and continuous speech recognition experiments respectively. Through the experiments, we have got the average 71.5%, 99.2% of the phoneme, word recognition accuracy when the state number is 2,000, respectively and the average 91.6% of the continuous speech recognition accuracy when the state number is 800. Also we haute carried out the word recognition experiments using the HTK (HMM Too1kit) which is performed the state tying, compared to share the parameters of the HM-Net topology design algorithm. In word recognition experiments, the HM-Net topology design algorithm has an average of 4.0% higher recognition accuracy than the context-dependent acoustic models generated by the HTK implying the effectiveness of it.