• 한국음향학회:학술대회논문집
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• 한국음향학회 1998년도 제15회 음성통신 및 신호처리 워크샵(KSCSP 98 15권1호)
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• pp.242-245
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• 1998

음성인식 시스템의 입력인 음성은 실제의 음성부분 외에도 주변잡음을 포함한 기침 소리, 문닫는 소리, 책장 넘기는 소리등과 같은 사용자에 의해서 발생될 수 있는 다양한 종류의 비음성을 포함할 수 있다. 특히 에너지가 큰 비음성을 포함하는 경우 기존의 끝점검출 알고리듬만으로는 음성부분만의 정확한 검출이 어렵게 되고 이는 음성인식 시스템의 성능을 저하시키는 주요 원인이 된다. 본 논문에서는 음성 발생시 일어날 수 있는 비음성들에 대해서 조사하고 이러한 비음성이 포함될 때 음성부분만의 정확한 검출을 가능하게 하는 알고리듬을 제시하였다. 사용된 파라미터로는 자기상관법에 의해 얻어지는 피치정보와 웨이브렛 영역에서의 에너지로써 비교적 낮은 신호대 잡음비에서도 음성부 검출을 가능하게 하였다.

• The Journal of the Acoustical Society of Korea
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• 제16권3E호
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• pp.14-18
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• 1997

One fo the major problems in speech recognition is the mismatch between training and testing environments. Recently, SNR normalization technique, which normalizes the dynamic range of frequency channels in mel-scaled filterbank, was proposed[1]. While it showed improved robustness against additive noise, it requires a reliable speech detection mechanism and several adaptation parameters to be optimized. In this paper, we propose a modified SNR normalization technique. In this technique, we take simply the maximum of filterbank output and predetermined masking constant for each frequency band. According to the speaker-independent isolated word recognition in car noise environments, proposed modification yields better recognition performance that the original SNR normalization method, with rather reduced complexity.

• The Journal of the Acoustical Society of Korea
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• 제16권3E호
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• pp.19-23
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• 1997

The goal of postprocessing in automatic speech recognition is to improve recognition performance by utterance verification at the output of recognition stage. It is focused on the effective rejection of out-of vocabulary words based on the confidence score of hypothesized candidate word. We present two methods for computing confidence scores. Both methods are based on the distance between each observation vector and the representative code vector, which is defined by the most likely code vector at each state. While the first method employs simple time normalization, the second one uses a normalization technique based on the concept of on-line garbage mode[1]. According to the speaker independent isolated words recognition experiment with discrete density HMM, the second method outperforms both the first one and conventional likelihood ratio scoring method[2].

• 전자공학회논문지B
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• 제32B권5호
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• pp.765-776
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• 1995

This paper describes a speaker adaptation method to improve the recognition performance of MLP(multiLayer Perceptron) based HMM(Hidden Markov Model) speech recognizer. In this method, we use lst-order linear transformation network to fit data of a new speaker to the MLP. Transformation parameters are adjusted by back-propagating classification error to the transformation network while leaving the MLP classifier fixed. The recognition system is based on semicontinuous HMM's which use the MLP as a fuzzy vector quantizer. The experimental results show that rapid speaker adaptation resulting in high recognition performance can be accomplished by this method. Namely, for supervised adaptation, the error rate is signifecantly reduced from 9.2% for the baseline system to 5.6% after speaker adaptation. And for unsupervised adaptation, the error rate is reduced to 5.1%, without any information from new speakers.

• 전자공학회논문지B
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• 제32B권8호
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• pp.1110-1118
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• 1995

Over the past few years, a wide variety of techniques have been developed which make a reliable recognition of speech signal. Multi-layer perceptron(MLP) which has excellent pattern recognition properties is one of the most versatile networks in the area of speech recognition. This paper describes an automatic speech recognition system which use both MLP and lexicon. In this system., the recognition is performed by a network search algorithm which matches words in lexicon to MLP output scores. We also suggest a recognition algorithm which incorperat durational information of each phone, whose performance is comparable to that of conventional continuous HMM(CHMM). Performance of the system is evaluated on the database of 26 vocabulary size from 9 speakers. The experimental results show that the proposed algorithm achieves error rate of 7.3% which is 5.3% lower rate than 12.6% of CHMM.

• 전자공학회논문지B
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• 제31B권2호
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• pp.28-34
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• 1994

In this paper, a new time-domain parameter extraction method for speech recognition is proposed. The suggested emthod is based on the fact that the local peak-to-peak interval, i.e., the interval between maxima and minima of speech waveform is closely related to the frequency component of the speech signal. The parameterization is achieved by a sort of filter bank technique in the time domain. To test the proposed parameter extraction emthod, an isolated word recognizer based on Vector Quantization and Hidden Markov Model was constructed. As a test material, 22 words spoken by ten males were used and the recognition rate of 92.9% was obtained. This result leads to the conclusion that the new parameter extraction method can be used for speech recognition system. Since the proposed method is processed in the time domain, the real-time parameter extraction can be implemented in the class of personal computer equipped onlu with an A/D converter without any DSP board.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.675-678
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• 1999

본 논문에서는 음소별 코드북 개수의 선택과 벡터 양자화에 따른 음소 인식률과 고립단어 인식률에 대하여 다룬다. 음성모델은 이산 확률 밀도를 갖는 DHMM(Discrete Hidden Markov Model)을 사용하였으며, 코드북 생성과 벡터 양자화 알고리즘으로는 K-means 알고리즘과 LBG(Linde, Buzo, Gray) 알고리즘을 사용하였다 음소별 코드북 개수와 벡터 양자화를 최적화함으로써 음소 인식률을 향상시킬 수 있으며, 그 결과 안정된 고립단어 인식률을 얻을 수 있다.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2005년도 추계 종합학술대회 논문집
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• pp.364-370
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• 2005

다층 퍼셉트론의 학습을 빠르게 하기 위한 방법으로 층별 학습이 제안되었었다. 이 방법에서는 각 층별로 주어진 오차함수를 최적화 방법을 사용하여 감소시키도록 학습이 이루어진다. 이 경우 중간층 오차함수가 학습의 성능에 큰 영향을 미치는 데, 이 논문에서는 층별 학습의 성능을 개선하기 위한 중간층 오차함수를 제안한다. 이 중간층 오차함수는 출력층 오차함수에서 중간층 가중치의 학습에 관계된 성분을 유도하는 형태로 제안된다. 제안한 방법은 필기체 숫자 인식과 고립단어인식 문제의 시뮬레이션으로 효용성을 확인하였다.

• 음성과학
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• 제15권4호
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• pp.85-96
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• 2008

Automatic speech recognition has severe performance degradation under noisy environments. To cope with the noise problem, many methods have been proposed. Most of them focused on noise-robust features or model adaptation. However, researchers have overlooked utterance verification (UV) under noisy environments. In this paper we discuss UV problems based on the normalized confidence measure. First, we show that UV performance is also degraded in noisy environments with the experiments of an isolated word recognition. Then we observe how the degradation of UV performances is suffered. Based on the UV experiments we propose a modeling method of the statistics of phone confidences using sigmoid functions. For obtaining the parameters of the sigmoidal models, the particle swarm optimization (PSO) is adopted. The proposed method improves 20% rejection performance. Our experimental results show that the PSO-NCM can apply noise speech recognition successfully.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2006년도 추계학술대회 발표논문집
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• pp.3-6
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• 2006

This paper proposes a confidence measure employed for utterance verification in noisy environments. Most of conventional approaches estimate the proper threshold of confidence measure and apply the value to utterance rejection in recognition process. As such, their performance may degrade for noisy speech since the threshold can be changed in noisy environments. This paper presents further robust confidence measure based on the multi-pass confidence measure. The isolated word recognition based experimental results demonstrate that the proposed method outperforms conventional approaches as utterance verifier.