• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1126-1130
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• 2010

Speech production can be viewed as a filtering operation in which a sound source excites a vocal tract filter. The vocal tract is modeled as a chain of cylinders of varying cross-sectional area in linear prediction acoustic tube modeling. In this modeling the most common implementation assumes equal length of tube sections. Therefore, to model complex vocal tract shapes, a large number of tube sections are needed. This paper proposes a new vocal tract model with unfixed sectionlengths, which uses the reduced lattice filter for modeling the vocal tract. This model transforms the lattice filter to reduced structure and the Burg algorithm to modified version. When the conventional and the proposed models are implemented with the same order of linear prediction analysis, the proposed model can produce more accurate results than the conventional one. To implement a system within similar accuracy level, it may be possible to reduce the stages of the lattice filter structure. The proposed model produces the more similar vocal tract shape than the conventional one.

• Speech Sciences
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• v.2
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• pp.45-55
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• 1997

This paper describes the procedure of implementing an articulatory speech simulator, in order to model the human articulatory organs and to synthesize speech from this model after. Images required to construct the vocal tract model were obtained from MRI, they were then used to construct 2D and 3D vocal tract shapes. In this paper 3D vocal tract shapes were constructed by spatially concatenating and interpolating sectional MRI images. 2D vocal tract shapes were constructed and analyzed automatically into a digital filter model. Following this speech sounds corresponding to the model were then synthesized from the filter. All procedures in this study were using MATLAB.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.215-218
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• 2003

The method of vocal tract normalization has been known as a successful method for improving the accuracy of speech recognition. A frequency warping procedure based low complexity and maximum likelihood has been generally applied for vocal tract normalization. In this paper, we propose a new power spectrum warping procedure that can be improve on vocal tract normalization performance than a frequency warping procedure. A mechanism for implementing this method can be simply achieved by modifying the power spectrum of filter bank in Mel-frequency cepstrum feature(MFCC) analysis. Experimental study compared our Proposal method with the well-known frequency warping method. The results have shown that the power spectrum warping is better 50% about the recognition performance than the frequency warping.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2454-2456
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• 2002

Since articuratory synthesis models the human vocal organs as precise as possible, it is potentially the most desirable method to produce various words and languages. This paper proposes a new type of an articulatory synthesizer using Mermelstein vocal tract model and Kelly-Lochbaum digital filter. Previous researches have assumed that the length of the vocal tract or the number of its cross sections dose not vary while uttering. However, the continuous utterance can not be easily implemented under this assumption. The limitation is overcomed by "Buffer Rearrangement" for dynamic vocal tract in this paper.

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.73-78
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• 2012

Emotion recognition is an important technology in the filed of human-machine interface. To apply speech technology to emotion recognition, this study aims to establish a relationship between emotional groups and their corresponding voice characteristics by investigating various speech features. The speech features related to speech source and vocal tract filter are included. Experimental results show that statistically significant speech parameters for classifying the emotional groups are mainly related to speech sources such as jitter, shimmer, F0 (F0_min, F0_max, F0_mean, F0_std), harmonic parameters (H1, H2, HNR05, HNR15, HNR25, HNR35), and SPI.

• Speech Sciences
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• v.12 no.4
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• pp.43-52
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• 2005

The common form of pre-emphasis filter is $H(z)\;=\;1\;- az^{-1}$, where a typically lies between 0.9 and 1.0 in voiced signal. Also, this value reflects the degree of filter and equals R(1)/R(0) in Auto-correlation method. This paper proposes a new flattening algorithm to compensate the weaked high frequency components that occur by vocal cord characteristic. We used interval information of LSP to estimate formant frequency. After obtaining the value of slope and inverse slope using linear interpolation among formant frequency, flattening process is followed. Experimental results show that the proposed algorithm flattened the weaked high frequency components effectively. That is, we could improve the flattened characteristics by using interval information of LSP as flattening factor at the process that compensates weaked high frequency components.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.1-8
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• 2013

This paper proposes a new feature extraction algorithm to improve children's speech recognition in Korean. The proposed feature extraction algorithm combines three methods. The first method is on the vocal tract length normalization to compensate acoustic features because the vocal tract length in children is shorter than in adults. The second method is to use the uniform bandwidth because children's voice is centered on high spectral regions. Finally, the proposed algorithm uses a smoothing filter for a robust speech recognizer in real environments. This paper shows the new feature extraction algorithm improves the children's speech recognition performance.

• Phonetics and Speech Sciences
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• v.3 no.4
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• pp.85-91
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• 2011

Sasang constitution medicine utilizes voice characteristics to diagnose a person's constitution. To classify Sasang constitutional groups using speech information technology, this study aims at establishing the relationship between Sasang constitutional groups and their corresponding voice characteristics by investigating various speech feature variables. The speech variables include features related to speech source and vocal tract filter. Experimental results show that statistically significant correlation between voice characteristics and some speech feature variables is observed.

• Proceedings of the KSLP Conference
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• 1994.06b
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• pp.84-84
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• 1994

사람의 음성은 성대에서 성대음이 발성되어 성도(vocal tract)에서 공명되고 여과(filter)되어 생성된다. 성도로는 후두로부터 하인두강, 중인두강, 구강으로 이어지는 주된 통로와 하인두강, 중인두강, 상인두강, 비강으로 이어지는 보조적인 통로가 있다. 보통의 모음 발성 시에는 구강으로 통하는 통로가 주로 공명강으로 작용되며 비강 통로는 별 작용을 하지 않지만, 'ㄴ, ㅁ, o, ' 등의 비 자음을 발성할 때에는 비강통로가 주 공명강으로 작용된다. (중략)

• Phonetics and Speech Sciences
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• v.6 no.1
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• pp.71-76
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• 2014

In this paper voice quality of normal speech is qualitatively classified by five components of breathy, creaky, rough, nasal, and thin/thick voice. To determine whether a correlation exists between a subjective measure of voice and an objective measure of voice, each voice is perceptually evaluated using the 1/2/3 scale by speech processing specialists and acoustically analyzed using speech analysis tools such as the Praat, MDVP, and VoiceSauce. The speech parameters include features related to speech source and vocal tract filter. Statistical analysis uses a two-independent-samples non-parametric test. Experimental results show that statistical analysis identified a significant correlation between the speech feature parameters and the components of voice quality.