• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.270-275
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• 1998

It was assumed that the voice of cattle is generated with the virtual white noise through the digital filter called the linear prediction filter, and filter parameters (prediction coefficients) were estimated by the maximum entropy method (MEM) , using the sound signal of the animal . The feature planes were defined by the pairs of two parameters selected appropriately from these parameters. The cattle voices were divided into three levels, that is the high, medium and low levels according to their total power equivalent to the variances of the sound signal . It was found that the straight lines could be used for recognizing tow cow and one calf for high level voices. For high and medium level voices, however, it was difficult or impossible to recognize individual cattle on the parameters planes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.445-451
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• 2010

This paper presents a threshold adaptation based voice query transcription scheme for music information retrieval. The proposed scheme analyzes monophonic voice signal and generates its transcription for diverse music retrieval applications. For accurate transcription, we propose several advanced features including (i) Energetic Feature eXtractor (EFX) for onset, peak, and transient area detection; (ii) Modified Windowed Average Energy (MWAE) for defining multiple small but coherent windows with local threshold values as offset detector; and finally (iii) Circular Average Magnitude Difference Function (CAMDF) for accurate acquisition of fundamental frequency (F0) of each frame. In order to evaluate the performance of our proposed scheme, we implemented a prototype music transcription system called AMT2 (Automatic Music Transcriber version 2) and carried out various experiments. In the experiment, we used QBSH corpus [1], adapted in MIREX 2006 contest data set. Experimental result shows that our proposed scheme can improve the transcription performance.

• Journal of the Korea Society of Computer and Information
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• v.24 no.7
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• pp.93-100
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• 2019

In this paper, we propose an intelligent marketing service system that can provide custom advertisements and events to both businesses and customers by identifying the location and contents using the ultrasonic signals and feature information in voice signals. We also develop the encoding and decoding algorithm of ultrasonic signals for this system and analyze the performance evaluation results. With the development of the hyper-connected society, the on-line marketing has been activated and is growing in size. Existing store marketing applications have disadvantages that customers have to find out events or promotional materials that the headquarters or stores throughusing the corresponding applications whenever they visit them. To solve these problems, there are attempts to create intelligent marketing tools using GPS technology and voice recognition technology. However, this approach has difficulties in technology development due to accuracy of location and speed of comparison and retrieval of voice recognition technology, and marketing services for customer relation are also much simplified.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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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 Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.59-62
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• 2005

Voice activity detection (VAD) is important in many areas of speech processing technology. Speech/nonspeech discrimination in noisy environments is a difficult task because the feature parameters used for the VAD are sensitive to the surrounding environments. Thus the VAD performance is severely degraded at low signal-to-noise ratios (SNRs). In this paper, a new VAD algorithm is proposed based on the degree of voicing and Quantile SNR (QSNR). These two feature parameters are more robust than other features such as energy and spectral entropy in noisy environments. The effectiveness of proposed algorithm is evaluated under the diverse noisy environments in the Aurora2 DB. According to out experiment, the proposed VAD outperforms the ETSI Advanced Frontend VAD.

• Journal of Industrial Technology
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• v.29 no.A
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• pp.95-100
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• 2009

In this paper, we implemented a speaker-independent speech recognizer using the TMS320F28335 DSP which is optimized for control applications. For this implementation, we used a small-sized commercial DSP module and developed a peripheral board including a codec, signal conditioning circuits and I/O interfaces. The speech signal digitized by the TLV320AIC23 codec is analyzed based on MFCC feature extraction methed and recognized using the continuous-density HMM. Thanks to the internal SRAM and flash memory on the TMS320F28335 DSP, we did not need any external memory devices. The internal flash memory contains ADPCM data for voice response as well as HMM data. Since the TMS320F28335 DSP is optimized for control applications, the recognizer may play a good role in the voice-activated control areas in aspect that it can integrate speech recognition capability and inherent control functions into the single DSP.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.397-402
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• 2007

In this paper, we apply a support vector machine(SVM) that incorporates an optimized nonlinear decision rule over different sets of feature vectors to improve the performance of statistical model-based voice activity detection(VAD). Conventional method performs VAD through setting up statistical models for each case of speech absence and presence assumption and comparing the geometric mean of the likelihood ratio (LR) for the individual frequency band extracted from input signal with the given threshold. We propose a novel VAD technique based on SVM by treating the LRs computed in each frequency bin as the elements of feature vector to minimize classification error probability instead of the conventional decision rule using geometric mean. As a result of experiments, the performance of SVM-based VAD using the proposed feature has shown better results compared with those of reported VADs in various noise environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.34-39
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• 2009

As a subjective recognition effect, human's emotion has impulsive characteristic and it expresses intentions and needs unconsciously. These are pregnant with information of the context about the ubiquitous computing environment or intelligent robot systems users. Such indicators which can aware the user's emotion are facial image, voice signal, biological signal spectrum and so on. In this paper, we generate the each result of facial and voice emotion recognition by using facial image and voice for the increasing convenience and efficiency of the emotion recognition. Also, we extract the feature which is the best fit information based on image and sound to upgrade emotion recognition rate and implement Multi-Modal Emotion recognition system based on feature fusion. Eventually, we propose the possibility of the ubiquitous computing service reasoning method based on Bayesian Network and ubiquitous context scenario in the ubiquitous computing environment by using result of emotion recognition.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.87-92
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• 2023

With the continuous development of the speech recognition system, the recognition rate for speech has developed rapidly, but it has a disadvantage in that it cannot accurately recognize the voice due to the noise generated by mixing various voices with the noise in the use environment. In order to increase the vocabulary recognition rate when processing speech with environmental noise, noise must be removed. Even in the existing HMM, CHMM, GMM, and DNN applied with AI models, unexpected noise occurs or quantization noise is basically added to the digital signal. When this happens, the source signal is altered or corrupted, which lowers the recognition rate. To solve this problem, each voice In order to efficiently extract the features of the speech signal for the frame, the MFCC was improved and processed. To remove the noise from the speech signal, the noise removal method using the Gaussian model applied noise deviation estimation was improved and applied. The performance evaluation of the proposed model was processed using a cross-correlation coefficient to evaluate the accuracy of speech. As a result of evaluating the recognition rate of the proposed method, it was confirmed that the difference in the average value of the correlation coefficient was improved by 0.53 dB.