• 한국음향학회지
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• 제33권4호
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• pp.268-272
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• 2014

본 논문에서는 MSVQ(Multi-Section Vector Quantization)와 시간지연 회귀 신경회로망(TDRNN)을 이용한 하이브리드 구조의 음성인식 방법을 제안한다. MSVQ는 음성의 길이를 일정한 구간 수로 정규화한 코드북을 생성하고, 시간지연 회귀 신경회로망은 이 코드북을 이용하여 음성을 인식한다. 시간지연 회귀 신경회로망은 음성의 시계열 문맥정보를 잘 학습할 수 있는 구조로 구성되었다. 음성특징으로 인지선형예측(PLP) 계수가 사용되었다. 음성인식 실험을 수행한 결과 MSVQ/TDRNN 음성인식기는 97.9 %의 화자독립 음성 인식률을 보였다.

• 한국음향학회지
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• 제13권2호
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• pp.5-12
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• 1994

본 논문은 One Stage MSVQ/DP를 제안하여 단어 인식을 수행하였다. 인식 대상 어휘로는 대학교 행정부서명 40개를 선정 하였고 인식을 위한 특징 파라메타로는 10차 LPC 켑스트럼 계수를 사용하였다. 본 연구에서 제안하는 One Stage MSVQ/DP 인식 시스템 이외에도 같은 데이터 상에서 LBDTW인식 시스템, One Stage DP 인식시스템에 의한 음성인식 실험을 수행하였다. LBDTW와 One Stage DP알고리즘에 의한 인식율은 $83.3\%$와 $87.5\%$였으며 본 연구에서 제안한 MSVQ/DP에 의한 인식율은 $91.6\%$였다.

• 한국음향학회지
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• 제28권3호
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• pp.251-259
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• 2009

본 논문에서는 하이드로폰으로 측정한 지진음 데이터를 가지고 20 MSVQ 알고리즘을 이용하여 자연지진음과 인공지진음을 식별하였다. 지진음 식별을 위한 특징 파라미터로는 스펙트럼 대역별 에너지, MFCC를 사용하였으며, 실험을 통하여 식별에 적합한 특징 파라미터 차수를 결정하였다. 2개의 특징 파라미터를 가지고 20 MSVQ 알고리즘으로 식별한 결과 MFCC를 사용하였을 경우에 99.9%, 스펙트럼 에너지 파라미터는 83.9%의 식별결과를 얻었다. 본 논문에서 제안한 파라미터와 알고리즘을 사용하여 지진음을 식별한 결과 성능이 매우 우수함을 확인하였다.

• 한국통신학회논문지
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• 제16권2호
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• pp.196-205
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• 1991

본 논문은 화자 독립의 단독이 언직에 관한 연구로 기존의 MSVQ(multisection vector quantization) 일질시스템을 개선한 새로운 MSVQ 시스템을 제안한다. 제안된 내용은 기존의 시스템과는 달리 인식시 시험패턴의 구간 수를 표준패턴의 구간 수보다 한 구간 더 늘리는 것이다. 이 방법에 의한 실험시 인식 대상으로는 146개의 DDD 지역망을 선택했으며, 특징 파라베타로는 12사 LPC 스트럼(cepstrum) 계수를 사용했고 코드북 지정석 중심점 구하는 방법으로 MINSUM과 MINIMAX기법을 사용하였다. 실험 결과에 의하면 DTW(dynamic time warping) 패턴 매칭 방법, VQ(vector quantization)에 의한 방법은 물론 기존의 MSVQ 방법보다 계산량이 감소함과 동시에 더 높은 인식율을 얻을 수 있었다. 수 있었다.

• 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.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1987년도 춘계학술발표회 논문집
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• pp.35-38
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• 1987

The purpose of this paper is to compare a class of vector quantization techniques which include GVQ(Genera VQ) MSVQ(Mean separated VQ) and DCT_VQ The VQ techniques are applied to six images and both subjective and objective performance comparison are made The results indicate that the transform domain approach(DCT_VQ) yields more syable results than the spatial domain method (GVQ, MSVQ)

• 대한전자공학회논문지
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• 제27권9호
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• pp.1468-1475
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• 1990

In this paper, isolated words are recognized using multisection VQ and HMM. As recognition vocabuaries, 20 area-name which is uttered 5 times by 3 speakers is selected. In generating codebook, we devide recognition vocabulary into equal length, section, and make standard VQ codebook to each section and calculate observation by section and than recognize isolated words by HMM training. Multisection VQ codebook has time information and as observation is calculated by eacy section, computation is lesser and recongnition rate is higher than by whole codword. As a result, it is proved that recognition rate is higher in case of HMM using multisection VQ codebook.

• 전자공학회논문지B
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• 제28B권12호
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• pp.68-74
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• 1991

This paper proposes a HMM model based on fuzzy, as a method on the speech recognition of speaker-independent. In this recognition method, multi-observation sequences which give proper probabilities by fuzzy rule according to order of short distance from VQ codebook are obtained. Thereafter, the HMM model 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. The vocabularies for recognition experiment are 146 DDD are names, and the feature parameter is 10S0thT LPC cepstrum coefficients. Besides the speech recognition experiments of proposed model, for comparison with it, we perform the experiments by DP, MSVQ and general HMM under same condition and data. Through the experiment results, it is proved that HMM model using fuzzy proposed in this paper is superior to DP method, MSVQ and general HMM model in recognition rate and computational time.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1039-1045
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• 1994

The speech recognition systems using VQ have usually the problem decreasing recognition rate, MSVQ assigning the dissimilar vectors to a segment. In this paper, applying One-stage DMS/DP algorithm to the recognition experiments, we can solve these problems to what degree. Recognition experiment is peformed for Korean DDD area names with DMS model of 20 sections and word unit template. We carried out the experiment in speaker dependent and speaker independent, and get a recognition rates of 97.7% and 81.7% respectively.