• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.690-693
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• 2002

According to the growth of communication biz, embedded market rapidly developing in domestic and overseas. Embedded system can be used in various way such as wire and wireless communication equipment or information products. There are lots of developing performance applying speech recognition to embedded system, for instance, PDA, PCS, CDMA-2000 or IMT-2000. This study implement minimum memory of speech recognition engine and DB for apply real time embedded system. The implement measure of speech recognition equipment to fit on embedded system is like following. At first, DC element is removed from Input voice and then a compensation of high frequency was achieved by pre-emphasis with coefficients value, 0.97 and constitute division data as same size as 256 sample by lapped shift method. Through by Levinson - Durbin Algorithm, these data can get linear predictive coefficient and again, using Cepstrum - Transformer attain feature vectors. During HMM training, We used Baum-Welch reestimation Algorithm for each words training and can get the recognition result from executed likelihood method on each words. The used speech data is using 40 speech command data and 10 digits extracted form each 15 of male and female speaker spoken menu control command of Embedded system. Since, in many times, ARM CPU is adopted in embedded system, it's peformed porting the speech recognition engine on ARM core evaluation board. And do the recognition test with select set 1 and set 3 parameter that has good recognition rate on commander and no digit after the several tests using by 5 proposal recognition parameter sets. The recognition engine of recognition rate shows 95%, speech commander recognizer shows 96% and digits recognizer shows 94%.

• 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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.192-195
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• 2006

In this paper, we discuss the user-customized interaction for intelligent home environments. The interactive system is based upon the integrated techniques using speech recognition and fingerprint verification. For essential modules, the speech recognition and synthesis were basically used for a virtual interaction between the user and the proposed system. In experiments, particularly, the real-time speech recognizer based on the HM-Net(Hidden Markov Network) was incorporated into the integrated system. Besides, the fingerprint verification was adopted to customize home environments for a specific user. In evaluation, the results showed that the proposed system was easy to use for intelligent home environments, even though the performance of the speech recognizer was not better than the simulation results owing to the noisy environments

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.32.3-32
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• 2001

This paper proposes a layered protocol to interpret voice commands of the user´s own language to a machine, to control it in real time. The layers consist of speech signal capturing layer, lexical analysis layer, interpretation layer and finally activation layer, where each layer tries to mimic the human counterparts in command following. The contents of a continuous voice command are captured by using Hidden Markov Model based speech recognizer. Then the concepts of Artificial Neural Network are devised to classify the contents of the recognized voice command ...

• 대한음성학회:학술대회논문집
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• 대한음성학회 2002년도 11월 학술대회지
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• pp.183-186
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• 2002

The speech detection is one of the important problems in real-time speech recognition. The accurate detection of speech boundaries is crucial to the performance of speech recognizer. In this paper, we propose a speech detector based on Mel-band selection through training. In order to show the excellence of the proposed algorithm, we compare it with a conventional one, so called, EPD-VAA (EndPoint Detector based on Voice Activity Detection). The proposed speech detector is trained in order to better extract keyword speech than other speech. EPD-VAA usually works well in high SNR but it doesn't work well any more in low SNR. But the proposed algorithm pre-selects useful bands through keyword training and decides the speech boundary according to the energy level of the sub-bands that is previously selected. The experimental result shows that the proposed algorithm outperforms the EPD-VAA.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2005년도 추계학술대회 논문집
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• pp.282-285
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• 2005

This paper discusses the interactive system for smart home environments. In order to realize this, the main emphasis of the paper lies on the description of the multiple signal processing on the basis of the technologies such as fingerprint recognition, video signal processing, speech recognition and synthesis. For essential modules of the interactive system, we adopted the motion detector based on the changes of brightness in pixels as well as the fingerprint identification for adapting home environments to the inhabitants. In addition, the real-time speech recognizer based on the HM-Net(Hidden Markov Network) and the speech synthesis were incorporated into the overall system for interaction between user and system. In experimental evaluation, the results showed that the proposed system was easy to use because the system was able to give special services for specific users in smart home environments, even though the performance of the speech recognizer was not better than the simulation results owing to the noisy environments.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.395-396
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• 2007

ETRI 음성/언어정보연구센터에서는 분산음성인식을 위해 메모리를 작게 사용하며 속도가 빠른 음소인식기를 개발 중이다. 음향 모델, 언어 모델, 탐색 네트워크 등 고정되어 있는 정보는 인식기를 수행하기 이전에 미리 binary 형태로 구축하여 ROM 형태로 저장함으로써 실제 사용해야 할 RAM 용량을 대폭 줄일 수 있었다. Tied state에 기반한 triphone 모델에서는 unique HMM 만을 사용함으로써 인식시간 및 메모리 사용량을 대폭 줄일 수 있었다. Monophone 인식기의 경우 RAM 사용량이 179KB였으며, triphone 인식기의 경우 435KB의 RAM 사용량과 RTF(Real Time Factor) 0.02를 확인하였다.

• 전자공학회논문지B
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• 제33B권11호
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• pp.97-105
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• 1996

This paper is a study on the implementation of speech recognition mobile robot to which the interactive speech recognition techniques is applied. The speech command uttered the sentential connected word and is asserted through the wireless mic system. This speech signal transferred LPC-cepstrum and shorttime energy which are computed from the received signal on the DSP board to notebook PC. In notebook PC, DP matching technique is used for recognizer and the recognition results are transferred to the motor control unit which output pulse signals corresponding to the recognized command and drive the stepping motor. Grammar network applied to reduce the recognition speed of the recogniger, so that real time recognition is realized. The misrecognized command is revised by interface revision through the conversation with mobile robot. Therefore, user can move the mobile robot to the direction which user wants.

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.273-278
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• 2004

본 논문은 주인식기로 흔히 사용되는 HMM 인식 알고리즘을 보완하기 위한 방법으로 회귀신경회로망(Recurrent neural networks : RNN)을 적용하였다. 이 회귀신경회로망 중에서 실 시간적으로 동작이 가능하게 한 방법인 다층회귀신경예측 모델 (Multi-layer Recurrent Neural Prediction Model : MRNPM)을 사용하여 학습 및 인식기로 구현하였으며, HMM과 MRNPM 을 이용하여 Hybrid형태의 주 인식기로 설계하였다. 설계된 음성 인식 알고리즘을 잘 구별되지 않는 한국어 숫자음(13개 단어)에 대해 화자 독립형으로 인식률 테스트 한 결과 기존의 HMM인식기 보다 5%정도의 인식률 향상이 나타났다. 이 결과를 이용하여 실제 DSP(TMS320C6711) 환경 내에서 최적(인식) 코드만을 추출하여 임베디드 음성 인식 시스템을 구현하였다. 마찬가지로 임베디드 시스템의 구현 결과도 기존 단독 HMM 인식시스템보다 향상된 인식시스템을 구현할 수 있게 되었다.

• 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%.