• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.28 no.2
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• pp.112-117
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• 2017

Background and Objectives : The purpose of this study was to classify patients with unilateral vocal fold paralysis according to their fixed location and to analysis the effects of two treatment methods by early voice therapy and injection laryngoplasty. Materials and Methods : Twenty patients who were classified as full abduction and slight abduction according to the position of paralysis were treated injection laryngoplasy, and 23 patients were treated by voice therapy. Twenty patients were treated injection laryngoplasy and 23 patients were treated voice therapy. Results were evaluated by acoustic analysis, electroglottography, cepstrum analysis before and after therapy. The voice therapy was conducted by improving the larynx movement and glottal contact, whilst removing hypertension of the supraglottic and use the breathing. Results : Significant improvement was found in the acoustic parameter, cepstrum parameter, and EGG before and after treatment in both groups. There was no significant difference between the two groups when compared before and after treatment to compare the effects of injection laryngoplasty and voice therapy. Conclusion : The initial treatments for unilateral vocal cord paralysis are injection laryngoplasty and voice therapy. however, there is no precise standard about which method should be applied first. Therefore, in this study, we tried to classify patients according to their paralysis position and then apply two methods. The results of this study suggest that voice therapy and Injection laryngoplasty at the initial stage is a very useful method to improve voice quality of vocal fold paralysis and improve laryngeal function.

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.25-28
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• 2003

The aim of this paper is to analyze the pathological voice by separating signal into periodic and aperiodic part. Separation was peformed recursively from the residual signal of voice signal. Based on initial estimation of aperiodic part of spectrum, aperiodic part is decided from the extrapolation method. Periodic part is decided by subtracting aperiodic part from the original spectrum. A parameter HNR is derived based on the separation. Parameter value statistics are compared with those of Jitter and Shimmer for normal, benign and malignant cases.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.12 no.2
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• pp.115-120
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• 2001

In this paper, we synthesized vowel /ae/ with voice synthesis program of Dr. Speech Science, and we also synthesized pathologic vowel /ae/ by some parameters such as high frequency gain (HFG), low frequency gain(LFG), pitch flutter(PF) which represents jitter value and flutter of amplitude(FA) which represents shimmer value, and grade ranked as mild, moderate and severe respectively. And then we analysed all pathologic voice by analysis program of Dr. Speech Science. We expect that this synthesized pathologic voices are useful for understanding the parameter such as noise, jitter and shimmer and feedback effect to patient with voice disorder.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.397-404
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• 2011

In this paper, we were carried out experiments to apply parameter of voice analysis to measure changing characteristic articulator according to inhale the helium gas. The helium gas was used to overcome air embolism nitrogen gas to deal a fatal blow in body nitrogen gas by diver. However, the helium gas has been much trouble interpretation about abnormal voice of diver to cause squeaky voice of low articulation. Therefor, we was carried out experiments about pitch and spectrogram measurement, analysis based on to influence in acoustic organs before and after of inhaled helium gas.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.181-184
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• 2000

The new method of feature extraction is proposed, considering the statistic feature of human voice, unlike the conventional methods of voice extraction. PCA(principal Component Analysis) is applied to this new method. PCA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. Then the new method is applied to real voice recognition to assess performance. When results of the number recognition in this paper and the conventional Mel-Cepstrum of voice feature parameter are compared, there is 0.5% difference of recognition rate. Better recognition rate is expected than word or sentence recognition in that less convergence time than the conventional method in extracting voice feature. Also, better recognition tate is expected when the optimum vector is used by statistic feature of data.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.313-318
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• 2015

A traditional speech enhancement methods distort the sound spectrum generated according to estimation of the remaining noise, or invalid noise is a problem of lowering the speech recognition performance. In this paper, we propose a speech detection method that convergence the sound energy distribution process and sound energy parameters. The proposed method was used to receive properties reduce the influence of noise to maximize voice energy. In addition, the smaller value from the feature parameters of the speech signal The log energy features of the interval having a more of the log energy value relative to the region having a large energy similar to the log energy feature of the size of the voice signal containing the noise which reducing the mismatch of the training and the recognition environment recognition experiments Results confirmed that the improved recognition performance are checked compared to the conventional method. Car noise environment of Pause Hit Rate is in the 0dB and 5dB lower SNR region showed an accuracy of 97.1% and 97.3% in the high SNR region 10dB and 15dB 98.3%, showed an accuracy of 98.6%.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.946-952
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• 2011

In term of inaccuracy of information and increasing channel occupancy time, the use of voice communication in Air Traffic Control has many problems. In order to improve it, ICAO proposed digital communication and ADS-B system that is more effective for voice communication in ATC. For improvement of effectiveness to add additional parameter to designated ADS-B In-Out data group, many studies being performed. In this paper, we analysis voice communication for reduce the communication traffic in ATC and simulate to add aiming altitude parameter for comparative effect analysis of communication traffic between pilot and controller. The result of the analysis were successfully validated that reduction of communication traffic in ADS-B environments.

• Speech Sciences
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• v.9 no.4
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• pp.253-263
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• 2002

In this paper, we propose new algorithm of speaker recognition which identifies the speaker using the information obtained by the intensive speech feature analysis such as pitch, intensity, duration, and formant, which are crucial parameters of individual voice, for candidates of high percentage of wrong recognition in the existing speaker recognition algorithm. For testing the power of discrimination of individual parameter, DTW (Dynamic Time Warping) is used. We newly set the range of threshold which affects the power of discrimination in speech verification such that the candidates in the new range of threshold are finally discriminated in the next stage of sound parameter analysis. In the speaker verification test by using voice DB which consists of secret words of 25 males and 25 females of 8 kHz 16 bit, the algorithm we propose shows about 1% of performance improvement to the existing algorithm.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.400-409
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• 2004

In this paper. a voice personality transformation method is proposed. which makes one person's voice sound like another person's voice. To transform the voice personality. vocal tract transfer function is used as a transformation parameter. Comparing with previous methods. the proposed method makes transformed speech closer to target speaker's voice in both subjective and objective points of view. Conversion between vocal tract transfer functions is implemented by classification of entire vector space followed by linear transformation for each cluster. LPC cepstrum is used as a feature parameter. A joint classification and transformation method is proposed, where optimum clusters and transformation matrices are simultaneously estimated in the sense of a minimum mean square error criterion. To evaluate the performance of the proposed method. transformation rules are generated from 150 sentences uttered by three male and on female speakers. These rules are then applied to another 150 sentences uttered by the same speakers. and objective evaluation and subjective listening tests are performed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.97-103
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• 2012

In this paper, we propose a novel voice activity detection (VAD) algorithm to effectively distinguish speech from nonspeech in various noisy environments. Global speech absence probability (GSAP) derived from likelihood ratio (LR) based on the statistical model is widely used as the feature parameter for VAD. However, the feature parameter based on conventional GSAP is not sufficient to distinguish speech from noise at low SNRs (signal-to-noise ratios). The presented VAD algorithm utilizes GSAP based on Teager energy (TE) as the feature parameter to provide the improved performance of decision for speech segments in noisy environment. Performances of the proposed VAD algorithm are evaluated by objective test under various environments and better results compared with the conventional methods are obtained.