• 대한음성학회지:말소리
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• 제45호
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• pp.117-126
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• 2003

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2000년도 International Symposium on Bioinformatics
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• pp.28-31
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• 2000

We describe a hidden Markov model, HMMTIR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear HMMs used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the database, and achieves a great reduction in parameters by representing overlapping motifs in a much more compact form. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.6% and backbone torsion angles better than any previously reported method, and predicts the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction. HMMSTR has been incorporated into a public, fully-automated protein structure prediction server.

• 음성과학
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• 제11권4호
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• 한국정보전자통신기술학회논문지
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• 제10권1호
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• pp.85-94
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• 2017

본 논문에서는 동물소리 인식시스템을 위하여 최대 빈도모델 탐색 알고리즘을 고안하고 이를 이용한 소리모델을 생성하는 방법을 제안하였다. 소리모델 생성 방법은 동물종의 소리 데이터로부터 학습과정, 비터비 탐색과정 및 최대 빈도모델 탐색과정을 반복하면서 HMM(Hidden Makcov Model)모델의 구조(상태의 수와 GMM의 수)를 탐색하여 최적의 인식률을 갖는 모델집합이 생성하는 방법이다. 최대 빈도모델 탐색 알고리즘은 입력 소리 데이터를 비터비(Viterbi) 알고리즘으로 탐색하여 모델리스트를 생성하고 이 리스트 중에서 최대 빈도수의 모델을 탐색하여 최종 인식결과로 결정하는 방법이다. 알고리즘에서 소리특징으로 MFCC(Mel Frequency Cepstral Coefficient), 모델형식으로 HMM을 이용하고 C# 프로그래밍언어로 구현 하였다. 알고리즘의 성능을 평가하기 위하여 27종의 동물소리를 선정하고 실험을 하였으며 27개의 HMM 모델집합이 97.29 퍼센트의 인식률로 생성됨을 확인하였다.

• The Journal of the Acoustical Society of Korea
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• 제21권1E호
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• pp.3-11
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• 2002

Broadcast news transcription is one of the hardest tasks in speech recognition because broadcast speech signals have much variability in speech quality, channel and background conditions. We developed a Korean broadcast news speech recognizer. We used a morpheme-based dictionary and a language model to reduce the out-of·vocabulary (OOV) rate. We concatenated the original morpheme pairs of short length or high frequency in order to reduce insertion and deletion errors due to short morphemes. We used a lexicon with multiple pronunciations to reflect inter-morpheme pronunciation variations without severe modification of the search tree. By using the merged morpheme as recognition units, we achieved the OOV rate of 1.7% comparable to European languages with 64k vocabulary. We implemented a hidden Markov model-based recognizer with vocal tract length normalization and online speaker adaptation by maximum likelihood linear regression. Experimental results showed that the recognizer yielded 21.8% morpheme error rate for anchor speech and 31.6% for mostly noisy reporter speech.

• 대한음성학회지:말소리
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• 제51호
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• pp.151-165
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• 2004

For educational and research purposes, a Korean speech recognition platform is designed. It is based on an object-oriented architecture and can be easily modified so that researchers can readily evaluate the performance of a recognition algorithm of interest. This platform will save development time for many who are interested in speech recognition. The platform includes the following modules: Noise reduction, end-point detection, met-frequency cepstral coefficient (MFCC) and perceptually linear prediction (PLP)-based feature extraction, hidden Markov model (HMM)-based acoustic modeling, n-gram language modeling, n-best search, and Korean language processing. The decoder of the platform can handle both lexical search trees for large vocabulary speech recognition and finite-state networks for small-to-medium vocabulary speech recognition. It performs word-dependent n-best search algorithm with a bigram language model in the first forward search stage and then extracts a word lattice and restores each lattice path with a trigram language model in the second stage.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1937-1943
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• 2005

The technology of emotion recognition is a crucial factor in day of ubiquitous that it provides various intelligent services for human. This paper intends to make the system which recognizes the human emotions based on 2-dimensional model with two bio signals, GSR and HRV. Since it is too difficult to make model the human's bio system analytically, as a statistical method, Hidden Markov Model(HMM) is used, which uses the transition probability among various states and measurable observation variance. As a result of experiments for each emotion, we can get average recognition rates of 64% for first HMM results and 55% for second HMM results

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.762-766
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• 2002

This paper describes the emotion recognition algorithm using HMM(Hidden Markov Model) method. The relation between the mechanic system and the human has just been unilateral so far. This is the why people don't want to get familiar with multi-service robots of today. If the function of the emotion recognition is granted to the robot system, the concept of the mechanic part will be changed a lot. Pitch and Energy extracted from the human speech are good and important factors to classify the each emotion (neutral, happy, sad and angry etc.), which are called prosodic features. HMM is the powerful and effective theory among several methods to construct the statistical model with characteristic vector which is made up with the mixture of prosodic features

• The Journal of the Acoustical Society of Korea
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• 제16권2E호
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• pp.55-61
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• 1997

This paper proposes a MSHMM(Multi-Section Hidden Markov Model) recognition method based on Fuzzy Concept, as a method on the speech recognition of speaker-independent. In this recognition method, training data are divided into several section and multi-observation sequences given proper probabilities by fuzzy rule according to order of short distance from MSVQ codebook per each section are obtained. Thereafter, the HMM per each section using this multi-observation sequences is generated, and in case of recognition, a word that has the most highest probability is selected as a recognized word. In this paper, other experiments to compare with the results of these experiments are implemented by the various conventional recognition methods(DP, MSVQ, DMS, general HMM) under the same data. Through results of all-round experiment, it is proved that the proposed MSHMM based on fuzzy concept is superior to DP method, MSVQ method, DMS model and general HMM model in recognition rate and computational time, and does not decreases recognition rate as 92.91% in spite of increment of speaker number.

• 전자공학회논문지S
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• 제34S권4호
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• pp.57-65
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• 1997

The purpose of this paper is to propose the HMM (hidden markov model) based on multi-observation sequence for the isolated word recognition. The proosed model generates the codebook of MSVQ by dividing each word into several sections followed by dividing training data into several sections. Then, we are to obtain the sequential value of multi-observation per each section by weighting the vectors of distance form lower values to higher ones. Thereafter, this the sequential with high probability value while in recognition. 146 DDD area names are selected as the vocabularies for the target recognition, and 10LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments by way of the proposed model, for the comparison with it, the experiments by DP, MSVQ, and genral HMM are made with the same data under the same condition. The experiment results have shown that HMM based on multi-observation sequence proposed in this paper is proved superior to any other methods such as the ones using DP, MSVQ and general HMM models in recognition rate and time.