• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.164-170
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• 2013

Recent years have been higher demands for automatic speech recognition (ASR) systems that are able to operate robustly in an acoustically noisy environment. This paper proposes an improved product hidden markov model (HMM) used for bimodal speech recognition. A two-dimensional training model is built based on dependently trained audio-HMM and visual-HMM, reflecting the asynchronous characteristics of the audio and video streams. A weight coefficient is introduced to adjust the weight of the video and audio streams automatically according to differences in the noise environment. Experimental results show that compared with other bimodal speech recognition approaches, this approach obtains better speech recognition performance.

• Phonetics and Speech Sciences
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• v.2 no.1
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• pp.71-75
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• 2010

The PESQ is an objective speech quality evaluation measure that is known to have a high correlation with a subjective speech quality measure such as MOS. To examine whether it could be useful as an objective quality measure of synthetic speech, we carried out both subjective evaluation tests with MOS and DMOS and an objective evaluation test with PESQ for HMM-based Korean synthetic speech signals and analyzed the correlation between them. Experimental results have shown that the PESQ has correlations of 0.87 with MOS and 0.92 with DMOS. It means that the PESQ holds much promise for evaluating the quality of synthetic Korean speech.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.281-281
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• 2000

This paper presents an implementation of speech recognition system for teaching an autonomous mobile robot. The use of human speech as the teaching method provides more convenient user-interface for the mobile robot. In this study, for easily teaching the mobile robot, a study on the autonomous mobile robot with the function of speech recognition is tried. In speech recognition system, a speech recognition algorithm using HMM(Hidden Markov Model) is presented to recognize Korean word. Filter-bank analysis model is used to extract of features as the spectral analysis method. A recognized word is converted to command for the control of robot navigation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.475-490
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• 2010

Cognitive Radio is an advanced enabling technology for the efficient utilization of vacant spectrum due to its ability to sense the spectrum environment. It is important to determine accurate spectrum utilization of the primary system in a cognitive radio environment. In order to define the spectrum utilization state, many CR systems use what is known as the quiet period (QP) method. However, even when using a QP, interference can occur. This causes reduced system throughput and contrary to the basic condition of cognitive radio. In order to reduce the interference time, a frequency-hopping algorithm is proposed here. Additionally, to complement the loss of throughput in the FH, a HMM-based channel prediction algorithm and a channel allocation algorithm is proposed. Simulations were conducted while varying several parameters. The findings show that the proposed algorithm outperforms conventional channel allocation algorithms.

• Journal of Information Technology Application
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• v.1 no.3_4
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• pp.1-23
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• 1999

본 연구에서는 HMM(Hidden Markov Model) 및 Levelbuilding 알고리즘을 이용하여 인식대상 음소열의 표본 집합(훈련패턴 집합)을 입력으로 하는 음성의 자동 분할 시스템을 구현하였다. 본 시스템은 자연스럽게 발음되어진 연결음 음성으로부터 표준 음소모델을 생성한다. 본 시스템의 구성은 초기화 과정, HMM학습과정 그리고 Levelbuilding을 이용한 분리 및 CLustering 과정으로 구성되어 있다. 초기화 과정에서는 제어 정보를 이용하여 훈련패턴 집합으로부터 초기 음소 집합 군을 생성한다. Levelbuilding을 이용한 분리 및 Clustering 단계에서는 음소 모델과 제어 정보를 이용하여 훈련패턴들을 음소 단위로 분리하고, 분리된 후보 음소들을 Clustering하여 음소집합 군을 생성한다. 음소모델의 구성에 변화가 없을 때까지 이 작업을 반복 수행하여 최적의 음소모델을 생성한다. 본 연구에서는 3개 이하의 숫자단어로 구성된 연결되어 음성 패턴을 대상으로 실험하였다. 연결단어에 대한 음소의 표준모델 생성과정에서 가장 중요한 처리인 훈련패턴의 자동분할 과정을 분석하기 위하여 각 반복과정에서 분리된 정보를 그래프로 도시화하여 확인하였다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.245-246
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• 2006

In this paper, we proposed using MFCC coefficients(Mel-Scaled Cepstral Coefficients) and a simple but efficient classifying method. Many other features: IAV, zero crossing, LPCC, $\ldot$ and their derivatives are also tested and compared with MFCC coefficients in order to find the best combination. GMM and HMM (Discrete and Continuous Hidden Markov Model), are studied as well in the hope that the use of continuous distribution and the temporal evolution of this set of features will improve the quality of emotion recognition.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.2093-2096
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• 2003

정보통신 구조의 확산 및 기술이 발전함에 따라 전산 시스템에 대한 침입과 피해가 증가되고 있는 실정이다. 이에 비정상행위 기반 침입탐지 시스템에 대한 연구가 활발히 진행되고 있는 가운데 특히, 시스템 호출 감사자료 척도에 은닉 마르코프 모델(HMM)로 모델링 하는 연구가 많이 이루어지고 있다. 하지만, 이는 일정한 임계값 이하의 비정상행위만을 감지할 뿐, 어떠한 유형의 침입인지를 판별하지 못한다. 본 논문에서는, 이러한 침입탐지 시스템의 맹점을 보완하기 위하여 Viterbi 알고리즘을 이용하여 상태 변화를 분석한 후, 어떤 유형의 침입이 발생하였는지를 판별하는 방법을 제안하고, 실험을 통해 제안한 시스템의 가능성을 보인다.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.301-314
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• 2012

In this paper, two new approaches to align English-Arabic sentences in bilingual parallel corpora based on the Multi-Class Support Vector Machine (MSVM) and the Hidden Markov Model (HMM) classifiers are presented. A feature vector is extracted from the text pair that is under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the Multi-Class Support Vector Machine and Hidden Markov Model. Another set of data was used for testing. The results of the MSVM and HMM outperform the results of the length based approach. Moreover these new approaches are valid for any language pairs and are quite flexible since the feature vector may contain less, more, or different features, such as a lexical matching feature and Hanzi characters in Japanese-Chinese texts, than the ones used in the current research.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.1-7
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• 2008

In this paper, a MSS-NOVO method that combines the HMM composition method with a noise reduction method is proposed for speech recognition in noisy environments. This combined method starts with noise reduction with modified spectral subtraction (MSS) to enhance the input noisy speech, then the noise and voice composition (NOVO) method is applied for making noise adapted models by using the noise in the non-utterance regions of the enhanced noisy speech. In order to evaluate the effectiveness of our proposed method, we compare MSS-NOVO method with other methods, i.e., SS-NOVO, MWF-NOVO. To set up the noisy speech for test, we add White noise to KLE 452 database with different SNRs range from 0dB to 15dB, at 5dB intervals. From the tests, MSS-NOVO method shows average improvement of 66.5% and 13.6% compared with the existing SS-NOVO method and MWF-NOVO method, respectively. Especially our proposed MSS-NOVO method shows a big improvement at low SNRs.

• Speech Sciences
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• v.8 no.3
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• pp.123-132
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• 2001

In recent years research on HCI technology has been very active and speech recognition is being used as its typical method. Its recognition, however, is deteriorated with the increase of surrounding noise. To solve this problem, studies concerning the multimodal HCI are being briskly made. This paper describes automated lipreading for bimodal speech recognition on the basis of image- and speech information. It employs audio-visual DB containing 1,074 words from 70 voice and tri-viseme as a recognition unit, and state tied HMM as a recognition model. Performance of automated recognition of 22 to 1,000 words are evaluated to achieve word recognition of 60.5% in terms of 22word recognizer.