• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3C
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• pp.234-239
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• 2008

For the efficient coding and transmission, the speech signal can be classified into three distinctive classes: voiced, unvoiced, and transition classes. At low bit rate coding below 4 kbit/s, conventional sinusoidal transform coders synthesize speech of high quality for the purely voiced and unvoiced classes, whereas not for the transition class. The transition class including plosive sound and abrupt voiced-onset has the lack of periodicity, thus it is often classified and synthesized as the unvoiced class. In this paper, the efficient algorithm for the transition class detection is proposed, which demonstrates superior detection performance not only for clean speech but for noisy speech. For the detected transition frame, phase information is transmitted instead of magnitude information for speech synthesis. From the listening test, it was shown that the proposed algorithm produces better speech quality than the conventional one.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.801-804
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• 2001

This article is concerned with automatic segmentation of two adjacent vowels for speech signals. All kinds of transition case of adjacent vowels can be characterized by spectrogram. Firstly the voiced-speech is extracted by the histogram analysis of vowel indicator which consists of wavelet low pass components. Secondly given phonetic transcription and transition pattern spectrogram, the voiced-speech portion which has consecutive vowels automatically segmented by the template matching. The cross-correlation function is adapted as a template matching method and the modified correlation coefficient is calculated for all frames. The largest value on the modified correlation coefficient series indicates the boundary of two consecutive vowel sounds. The experiment is performed for 154 vowel transition sets. The 154 spectrogram templates are gathered from 154 words(PRW Speech DB) and the 161 test words(PBW Speech DB) which are uttered by 5 speakers were tested. The experimental result shows the validity of the method.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.273-278
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• 2009

In this paper, we propose the spectral transition rate (STR) as a novel feature for speech and music discrimination (SMD). We observed that the spectral peaks of speech signal are gradually changing due to coarticulation effect. However, the sound of musical instruments in general tends to keep the peak frequencies and energies unchanged for relatively long period of time compared to speech. The STR of speech is much higher than that of music. The experimental results show that the STR based SMD method outperforms a conventional method. Especially, the STR based SMD gives relatively fast output without any performance degradation.

• Proceedings of the IEEK Conference
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• 2002.06d
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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.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.167-173
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• 2005

In a speech coding system using excitation source of voiced and unvoiced, it would be involved a distortion of speech quality in case coexist with a voiced and unvoiced consonants in a frame. So, I propose TSIUVC(Transition Segment Including Unvoiced Consonant) extraction method by using pitch pulses and Zero Crossing Rate in order to unexistent with a voiced and unvoiced consonants in a frame. And this paper present a TSIUVC approximate-synthesis method by using frequency band division. As a result, this method obtains a high quality approximation-synthesis waveform within TSIUVC by using frequency information of 0.547kHz below and 2.813kHz above. And the TSIUVC extraction rate was $91\%$ for female voice and $96.2\%$ for male voice respectively This method has the capability of being applied to a new speech coding of Voiced/Silence/TSIUVC, speech analysis, and speech synthesis.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.24-30
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• 2001

This article is concerned with automatic segmentation for Korean speech signals. All kinds of transition cases of phonetic units are classified into 3 types and different strategies for each type are applied. The type 1 is the discrimination of silence, voiced-speech and unvoiced-speech. The histogram analysis of each indicators which consists of wavelet coefficients and SVF (Spectral Variation Function) in wavelet coefficients are used for type 1 segmentation. The type 2 is the discrimination of adjacent vowels. The vowel transition cases can be characterized by spectrogram. Given phonetic transcription and transition pattern spectrogram, the speech signal, having consecutive vowels, are automatically segmented by the template matching. The type 3 is the discrimination of vowel and voiced-consonants. The smoothed short-time RMS energy of Wavelet low pass component and SVF in cepstral coefficients are adopted for type 3 segmentation. The experiment is performed for 342 words utterance set. The speech data are gathered from 6 speakers. The result shows the validity of the method.

• Speech Sciences
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• v.2
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• pp.9-23
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• 1997

Through iterative analysis and synthesis experiments on Korean monosyllables, the Korean text-to-speech system was implemented using the phoneme-based formant synthesis method. Since the formants of initial and final consonants in this system showed many variations depending on the medial vowels, the database for each phoneme was made up of formants depending on the medial vowels as well as duration information of transition region. These techniques were needed to improve the intelligibility of synthetic speech. This paper investigates also methods of concatenating the synthesis units to improve the quality of synthetic speech.

• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.195-200
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• 2013

This study investigates formant transition shapes of Korean front vowels produced by native speakers of Seoul Korean. Sixteen speakers (eight male and eight female speakers) produced [pVt] syllables where the vowels were [i, e, ɛ]. F1, F2, and F3 transition shapes were estimated by presenting formant values at 11 points by dividing the vowel duration into 10 different time intervals. The results indicated that the male and female speakers overall demonstrated similar formant transition shapes and measurement points arriving at the maximum and minimum formant values for the three front vowels. As for the vowels [e] and [ɛ], both male and female speakers showed similar formant values across the 11 measurement points and similar measurement points arriving at the maximum and minimum values, indicating that the two Korean vowels have been merged not only in the steady-state formant values, but also in the dynamic transition shapes.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.763-770
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• 2004

The important issue of emotion recognition from speech is a feature extracting and pattern classification. Features should involve essential information for classifying the emotions. Feature selection is needed to decompose the components of speech and analyze the relation between features and emotions. Specially, a pitch of speech components includes much information for emotion. Accordingly, this paper searches the relation of emotion to features such as the sound loudness, pitch, etc. and classifies the emotions by using the statistic of the collecting data. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1299-1304
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• 2000

In a speed coding system using excitation source of voiced and unvoiced, it would be involved a distortion of speech quality in case coexist with a voiced and an unvoiced consonants in a frame. So, I propose TSIUVC(Transition Segment Including UnVoiced Consonant) searching, extraction ad approximation-synthesis method in order to uncoexistent with a voiced and unvoiced consonants in a frame. This method based on a zerocrossing rate and pitch detector using FIR-STREAK Digital Filter. As a result, the extraction rates of TSIUVC are 84.8% (plosive), 94.9%(fricative), 92.3%(affricative) in female voice, and 88%(plosive), 94.9%(fricative), 92.3%(affricative) in male voice respectively, Also, I obain a high quality approximation-synthesis waveforms within TSIUVC by using frequency information of 0.547kHz below and 2.813kHz above. This method has the capability of being applied to speech coding of low bit rate, speech analysis and speech synthesis.