• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1996.06a
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• pp.136-139
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• 1996

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.727-734
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• 2002

A simple algorithm for discriminating voiced sounds in a speech is proposed in this paper. In addition to low-frequency energy and zero-crossing rate (ZCR), both of which have been widely used in the past for identifying voiced sounds, the proposed algorithm incorporates pitch variation to improve the discrimination rate. Based on TIMIT corpus, evaluation result shows an improvement of 13% in the discrimination of voiced phonemes over that of the traditional algorithm using only energy and ZCR.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.

• Proceedings of the Acoustical Society of Korea Conference
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• 1984.12a
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• pp.73-77
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• 1984

This paper reports on a Voiced-Unvoiced-Silence Classification of speech for Korean Speech Recognition. In this paper, it is describe a method which uses a Pattern Recognition Technique for classifying a given speech segment into the three classes. Best result is obtained with the combination using ZCR, P1, Ep and classification error rate is less than 1%.

• The Journal of the Acoustical Society of Korea
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• v.10 no.4
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• pp.5-11
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• 1991

This paper suggests a segmentation method of speech signal into phonemic units. The suggested segmentation system is speaker-independent and performed without anyprior information of speech signal. In segmentation process, we first divide input speech signal into purevoiced region and not pure voiced speech regions. After then we apply the second algorithm which segments each region into the detailed phonemic units by using the voiced detection parameters, i.e., the time variation of 0th LPC cepstrum coefficient parameter and the ZCR parameter. Types of speech, used to prove the availability of segmentation algorithm suggested in this paper, are the vocabulary composed of isolated words and continuous words. According to the experiments, the successful segmentation rate for 507 phonemic units involved in the total vocabulary is 91.7%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.533-536
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• 2015

Mobile music market is growing. However, services what are applied recently are inaccurate to recommend music that a user is worth to prefer. So, this paper suggests music recommend system. This system recommend music that users prefer analyzing music information and user's musical propensity and using collaborative filtering. This system classify genre and extract factors what can be get using STFT's ZCR, Spectral roll-off, Spectral flux. So similar musics are clustered by these factors. And then, after divide mood of music's lyric, it finally recommend music automatically using collaborative filtering.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.70-75
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• 2001

This paper proposes a syllabic segmentation method for the korean continuous speech. This method are formed three major steps as follows. (1) labeling the vowel, consonants, silence units and forming the Token the sequence of speech data using the segmental parameter in the time domain, pitch, energy, ZCR and PVR. (2) scanning the Token in the structure of korean syllable using the parser designed by the finite state automata, and (3) re-segmenting the syllable parts witch have two or more syllables using the pseudo-syllable nucleus information. Experimental results for the capability evaluation toward the proposed method regarding to the continuous words and sentence units are 73.5％, 85.9％, respectively.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.187-190
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• 2000

In this paper, implementation of speech Recognizer system, Separated from Personal computer. By using DSP, this intends to extend the voice recognizing, limited into PC because of amount of data and calculations. For this performance The thesis uses the real time End point detector and organizes no additional device between human and the system, characteristic vector are that detects End point and voice from absolute energy and ZCR, that uses 12 difference Cepstrum from LPC, that uses the method to compensate the process of pattern separating and pre-calculated standard pattern limitation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.12-19
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• 1980

A speech recognition system for separately pronounced Korean digits is described. The system is composed of four stages ; parameter extraction, segmentation by voiced-unovied analysis, formant tracking and pattern matching. Digit speech is segmented into an unvoiced segment and/or a voiced one using ZCR and energy measurements, then to estimate the first three formant frequencies a relatively simple formant tracking scheme is applied to the raw formant data extracted from linear prediction spectra. Finally, pattern matching is made using dynamic programmig method. Recognition experiment is carried out for 150 digit utterences spoken by three male speakers, and recgnition rate 94 % is obtained.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.881-883
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• 1987

This paper decribes a new technique for the generation of excitation sources in LPC system. We synthesize a speech signal using several excitation sources, according to residual signal energy and ZCR(zero Crossing Rate). One of the excitation sources mix the double differentiated glottal wave form source and noise source. As a result, we got improved speech signal than that produced by conventional LPC system.