• Speech Sciences
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• 제10권2호
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• pp.85-95
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• 2003

In speech signal processing, it is very important to detect the pitch exactly in speech recognition, synthesis and analysis. If we exactly pitch detect in speech signal, in the analysis, we can use the pitch to obtain properly the vocal tract parameter. It can be used to easily change or to maintain the naturalness and intelligibility of quality in speech synthesis and to eliminate the personality for speaker-independence in speech recognition. In this paper, we proposed a new pitch detection algorithm. First, positive center clipping is process by using the incline of speech in order to emphasize pitch period with a glottal component of removed vocal tract characteristic in time domain. And rough formant envelope is computed through peak-fitting spectrum of original speech signal infrequence domain. Using the roughed formant envelope, obtain the smoothed formant envelope through calculate the linear interpolation. As well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. Inverse fast fourier transform (IFFT) compute this flattened harmonics. After all, we obtain Residual signal which is removed vocal tract element. The performance was compared with LPC and Cepstrum, ACF. Owing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• The Journal of the Acoustical Society of Korea
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• 제32권2호
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• pp.167-173
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• 2013

This work examines classification of diphthongs, as part of a distinctive feature-based speech recognition system. Acoustic measurements related to the vocal tract and the voice source are examined, and analysis of variance (ANOVA) results show that vowel duration, energy trajectory, and formant variation are significant. A balanced error rate of 17.8% is obtained for 2-way diphthong classification on the TIMIT database, and error rates of 32.9%, 29.9%, and 20.2% are obtained for /aw/, /ay/, and /oy/, for 4-way classification, respectively. Adding the acoustic features to widely used Mel-frequency cepstral coefficients also improves classification.

• Annual Conference of KIPS
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1711-1714
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• 2015

스마트 디바이스가 사회와 소통할 수 있는 도구가 되었음에도 불구하고 아직까지 노인들이 사용하기에는 어려움이 있다. 여기에 음성인식 기술을 이용한 음성인터페이스를 활용함으로써 노인들의 스마트 디바이스에 대한 사용성을 높일 수 있다. 하지만 일반적인 음성인식 시스템은 청장년의 발성 스타일에 맞춰져 있기 때문에, 노화된 노인의 발성이 그대로 입력될 경우 음성인식률이 하락한다. 본 연구에서는 노인의 음절 별 발화속도가 일반적인 음성인식 시스템의 성능을 보증할 수 있는 범위를 벗어나는 경우가 많다는 분석 결과를 토대로 노인의 음절 별 발화속도를 조정한 결과 노인남녀 평균 음성인식률이 15.3% 상승하였다. 이처럼 노인의 음성인식 오류 원인들 중 하나인 발화속도의 재조정으로 음성 인식률을 높일 수 있는 토대를 마련하였다. 이는 노인들이 스마트 디바이스를 이용하여 쉽고 정확한 작업을 수행할 수 있게 됨으로써, 노인들의 사회 참여와 정보 획득이 용이해 지고 더 나아가 세대 간의 소통에도 이바지할 것으로 기대한다.

• Speech Sciences
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• 제15권1호
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• pp.75-83
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• 2008

The interior of an automobile is a very noisy environment with both stationary cruising noise and the reverberated music or speech coming out from the audio system. For robust speech recognition in a car environment, it is necessary to extract a driver's voice command well by removing those background noises. Since we can handle the music and speech signals from an audio system in a car, the reverberated music and speech sounds can be removed using an acoustic echo canceller. In this paper, we implement an acoustic echo canceller with robust double-talk detection algorithm using TMS-320C6711 DSK. First we developed the echo canceller on the PC for verifying the performance of echo cancellation, then implemented it on the TMS320C6711 DSK. For processing of one speech sample with 8kHz sampling rate and 256 filter taps of the echo canceller, the implemented system used only 0.035ms and achieved the ERLE of 20.73dB.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.381-384
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• 2002

In speech signal processing, it Is very important to detect the pitch exactly in speech recognition, synthesis and analysis. but, it is very difficult to pitch detection from speech signal because of formant and transition amplitude affect. therefore, in this paper, we proposed a pitch detection using the spectrum flattening techniques. Spectrum flattening is to eliminate the formant and transition amplitude affect. In time domain, positive center clipping is process in order to emphasize pitch period with a glottal component of removed vocal tract characteristic. And rough formant envelope is computed through peak-fitting spectrum of original speech signal in frequency domain. As a results, well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. After all, we obtain residual signal which is removed vocal tract element The performance was compared with LPC and Cepstrum, ACF 0wing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• Journal of Internet of Things and Convergence
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• 제9권3호
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• pp.87-92
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• 2023

As times develop, information becomes more diverse and methods of obtaining it become more diverse. About 80% of the amount of information gained in life is acquired through the visual sense. However, visually impaired people have limited ability to interpret visual materials. That's why Braille, a text for the blind, appeared. However, the Braille decoding rate of the blind is only 5%, and as the demand of the blind who want various forms of platforms or materials increases over time, development and product production for the blind are taking place. An example of product production is braille books, which seem to have more disadvantages than advantages, and unlike non-disabled people, it is true that access to information is still very difficult. In this paper, we designed a CNN-based Braille conversion and voice output device to make it easier for visually impaired people to obtain information than conventional methods. The device aims to improve the quality of life by allowing books, text images, or handwritten images that are not made in Braille to be converted into Braille through camera recognition, and designing a function that can be converted into voice according to the needs of the blind.

• Journal of the Korea Convergence Society
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• 제9권4호
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• pp.23-29
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• 2018

To reduce the accident rate caused by the delay of corrective action, which is common in the construction site, in order to shorten the time from correcting the existing system to the corrective action, I used a time similarity check to inform the inspectors of the problem in real time, modeling the system so that corrective action can be performed immediately on site, and studied a system that can actively cope with safety accidents. The research result shows that there is more than 90% opening effect and more than 60% safety accident reduction rate. I will continue to study more effective system combining voice recognition and deep learning based on this system.

• Journal of KIISE:Software and Applications
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• 제34권3호
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• pp.226-234
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• 2007

In this paper, we develop a speech recognition system using a throat microphone. The use of this kind of microphone minimizes the impact of environmental noise. However, because of the absence of high frequencies and the partially loss of formant frequencies, previous systems developed with those devices have shown a lower recognition rate than systems which use standard microphone signals. This problem has led to researchers using throat microphone signals as supplementary data sources supporting standard microphone signals. In this paper, we present a high performance ASR system which we developed using only a throat microphone by taking advantage of Korean Phonological Feature Theory and a detailed throat signal analysis. Analyzing the spectrum and the result of FFT of the throat microphone signal, we find that the conventional MFCC feature vector that uses a critical pass filter does not characterize the throat microphone signals well. We also describe the conditions of the feature extraction algorithm which make it best suited for throat microphone signal analysis. The conditions involve (1) a sensitive band-pass filter and (2) use of feature vector which is suitable for voice/non-voice classification. We experimentally show that the ZCPA algorithm designed to meet these conditions improves the recognizer's performance by approximately 16%. And we find that an additional noise-canceling algorithm such as RAST A results in 2% more performance improvement.

• Journal of Intelligence and Information Systems
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• 제17권1호
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제30권6C호
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• pp.505-511
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• 2005

Autonomic machines interacting with human should have capability to perceive the states of emotion and attitude through implicit messages for obtaining voluntary cooperation from their clients. Voice is the easiest and most natural way to exchange human messages. The automatic systems capable to understanding the states of emotion and attitude have utilized features based on pitch and energy of uttered sentences. Performance of the existing emotion recognition systems can be further improved withthe support of linguistic knowledge that specific tonal section in a sentence is related with the states of emotion and attitude. In this paper, we attempt to improve recognition rate of emotion by adopting such linguistic knowledge for Korean ending boundary tones into anautomatic system implemented using pitch-related features and multilayer perceptrons. From the results of an experiment over a Korean emotional speech database, the improvement of $4\%$ is confirmed.