• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.503-506
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• 2001

본 논문에서는 마이크 입력레벨 조절기의 구현과 이를 이용한 인식률의 향상을 다룬다. 마이크를 통한 음성 입력이 너무 작거나 너무 크면 인식률에 직접 영향을 미치므로 인식에 적합한 입력레벨로 조절할 필요가 있다. 자동 입력레벨 조절기의 구현을 위해 고려할 사항을 연구했으며, 이를 통해 PC환경의 입력레벨 조절기를 구현했다. 수집된 음성 데이터베이스는 켑스트럼 평균차감법(CMS)을 이용하여 채널왜곡을 보상했으며, 구현된 조절기를 이용하여 실험한 결과, 이용하지 않은 경우에 비해 약 50%의 오인식율을 줄일 수 있었다.

• 음성과학
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• 제10권4호
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• pp.117-124
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• 2003

Speaker recognition is the technology that confirms the identification of speaker by using the characteristic of speech. Such technique is classified into speaker identification and speaker verification: The first method discriminates the speaker from the preregistered group and recognize the word, the second verifies the speaker who claims the identification. This method that extracts the information of speaker from the speech and confirms the individual identification becomes one of the most efficient technology as the service via telephone network is popularized. Some problems, however, must be solved for the real application as follows; The first thing is concerning that the safe method is necessary to reject the imposter because the recognition is not performed for the only preregistered customer. The second thing is about the fact that the characteristic of speech is changed as time goes by, So this fact causes the severe degradation of recognition rate and the inconvenience of users as the number of times to utter the text increases. The last thing is relating to the fact that the common characteristic among speakers causes the wrong recognition result. The silence parts being included the center of speech cause that identification rate is decreased. In this paper, to make improvement, We proposed identification rate can be improved by removing silence part before processing identification algorithm. The methods detecting speech area are zero crossing rate, energy of signal detect end point and starting point of the speech and process DTW algorithm by using two methods in this paper. As a result, the proposed method is obtained about 3% of improved recognition rate compare with the conventional methods.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.195-198
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• 2003

The accuracy of automatic speech recognition system depends on the presence of background noise and speaker variability such as sex, intonation of speech, and speaking rate. Specially, the speaking rate of both inter-speaker and intra-speaker is a serious cause of mis-recognition. In this paper, we propose the compensation method of the speaking rate by the ratio of each vowel's length in a phrase. First the number of feature vectors in a phrase is estimated by the information of speaking rate. Second, the estimated number of feature vectors is assigned to each syllable of the phrase according to the ratio of its vowel length. Finally, the process of feature vector extraction is operated by the number that assigned to each syllable in the phrase. As a result the accuracy of automatic speech recognition was improved using the proposed compensation method of the speaking rate.

• 한국철도학회논문집
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• 제12권2호
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• pp.285-290
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• 2009

논문에서는 연속HMM(Continuos Hidden Markov Model)을 이용하여 실시간으로 발매기 명령어(314개 역명)를 인식 할 수 있도록 음성인식 시스템을 구현하였다. 특징 벡터로 39 MFCC를 사용하였으며, 인식률 향상을 위하여 895개의 tied-state 트라이폰 음소 모델을 구성하였다. 시스템 성능 평가 결과 다중 화자 종속 인식률은 99.24%, 다중화자 독립 인식률은 98.02%의 인식률을 나타내었으며, 실제 노이즈가 있는 환경에서 다중 화자 독립 실험의 경우 93.91%의 인식률을 나타내었다.

• 한국멀티미디어학회논문지
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• 제16권8호
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• pp.905-913
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• 2013

잡음에 강건한 음성 인식을 위한 주파수 변이를 이용한 PMC( Parallel Model Compensation Using Frequency-variant, FV-PMC) 방법은 인식시 입력음성에 혼입이 예상되는 잡음들을 평균 주파수 변이도를 임계치로 하여 몇 가지 잡음 군으로 분류한 후 각 잡음 군 별로 인식을 수행하는 방법이다. 이 방법은 기준 임계치를 이용하여 양호하게 분류된 잡음 음성들에 대해서는 매우 우수한 성능을 보이나, 미 분류된 잡음 음성들에 대해서는 기존의 PMC 방법에서와 같이 무잡음 모델과 결합하여 음성 인식을 수행함으로 인해 평균 음성 인식률이 낮아지는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 기존의 방법에서 사용하였던 평균주파수 임계치 방법 대신에 최대 우도를 부가하여 미분류를 방지함으로써 입력 잡음음성에 포함되는 잡음의 군별 잡음 분류 율을 높여 인식률을 제고하는 개선된 주파수 변이 PMC 인식방법을 제안하였다. Aurora 2.0 데이터베이스를 이용한 인식실험결과, 기존의 FV-PMC 방법에 비해 향상된 결과를 확인할 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.149-154
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• 2004

In this research, we selected the speech recognition to implement the electric wheelchair system as a method to control it by only using the speech and used DTW (Dynamic Time Warping), which is speaker-dependent and has a relatively high recognition rate among the speech recognitions. However, it has to have small memory and fast process speed performance under consideration of real-time. Thus, we introduced VQ (Vector Quantization) which is widely used as a compression algorithm of speaker-independent recognition, to secure fast recognition and small memory. However, we found that the recognition rate decreased after using VQ. To improve the recognition rate, we applied ART2 (Adaptive Reason Theory 2) algorithm as a post-process algorithm to obtain about 5% recognition rate improvement. To utilize ART2, we have to apply an error range. In case that the subtraction of the first distance from the second distance for each distance obtained to apply DTW is 20 or more, the error range is applied. Likewise, ART2 was applied and we could obtain fast process and high recognition rate. Moreover, since this system is a moving object, the system should be implemented as an embedded one. Thus, we selected TMS320C32 chip, which can process significantly many calculations relatively fast, to implement the embedded system. Considering that the memory is speech, we used 128kbyte-RAM and 64kbyte ROM to save large amount of data. In case of speech input, we used 16-bit stereo audio codec, securing relatively accurate data through high resolution capacity.

• 대한음성학회지:말소리
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• 제44호
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• pp.73-82
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• 2002

Channel distortion degrades the performance of speech recognizer in telephone environment. It mainly results from the bandwidth limitation and variation of transmission channel. Variation of channel characteristics is usually represented as baseline shift in the cepstrum domain. Thus undesirable effect of the channel variation can be removed by subtracting the mean from the cepstrum. In this paper, to improve the recognition performance of Korea connected digit telephone speech, channel compensation methods such as CMN (Cepstral Mean Normalization), RTCN (Real Time Cepatral Normalization), MCMN (Modified CMN) and MRTCN (Modified RTCN) are applied to the static MFCC. Both MCMN and MRTCN are obtained from the CMN and RTCN, respectively, using variance normalization in the cepstrum domain. Using HTK v3.1 system, recognition experiments are performed for Korean connected digit telephone speech database released by SITEC (Speech Information Technology & Industry Promotion Center). Experiments have shown that MRTCN gives the best result with recognition rate of 90.11% for connected digit. This corresponds to the performance improvement over MFCC alone by 1.72%, i.e, error reduction rate of 14.82%.

• 한국정보통신학회논문지
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• 제23권5호
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• pp.555-567
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• 2019

언어장애인, 언어 사용이 불편한 분들의 경우 조금의 잡음 환경에도 음성인식률이 저하되어 사회 생활시 어려움을 겪게 된다. 언어 사용 시 불편함을 장치로 개선시킴과 동시에, 언어 장애인의 발음 특성을 고려하여 단어 선정 시 자체 개선한 단어 선정 방법을 사용하여 280개 단어를 선정하였다. 실험에 사용된 MEMS 개발 장치는 재질, 유도선 종류, 길이, 방향을 고려하여 제작되었으며 잘못된 발음으로 인한 음성과 심한 소음에서 음성 인식률 향상을 위하여 개발된 MEMS 장치와 개발된 단어 선정 방법을 사용하여 개선시켰다. 개선 방법으론 새로운 단어 선정 방법과 mems 장치를 개선하여 진행하였으며 결과를 포함하였다.

• ETRI Journal
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• 제35권1호
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• pp.100-108
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• 2013

In this paper, a feature extraction (FE) method is proposed that is comparable to the traditional FE methods used in automatic speech recognition systems. Unlike the conventional spectral-based FE methods, the proposed method evaluates the similarities between an embedded speech signal and a set of predefined speech attractor models in the reconstructed phase space (RPS) domain. In the first step, a set of Gaussian mixture models is trained to represent the speech attractors in the RPS. Next, for a new input speech frame, a posterior-probability-based feature vector is evaluated, which represents the similarity between the embedded frame and the learned speech attractors. We conduct experiments for a speech recognition task utilizing a toolkit based on hidden Markov models, over FARSDAT, a well-known Persian speech corpus. Through the proposed FE method, we gain 3.11% absolute phoneme error rate improvement in comparison to the baseline system, which exploits the mel-frequency cepstral coefficient FE method.

• The Journal of the Acoustical Society of Korea
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• 제29권1E호
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• pp.45-53
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• 2010

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.