• Journal of Biomedical Engineering Research
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• v.8 no.2
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• pp.231-240
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• 1987

This Paper introduces a new design of motorized wheel chair for the disabled, which is intended to improve the quality of the disabled's indoor life. This vehicle was based on high manoeuvrability of the omnidirectional drive and saftey. Usually, the vehicle controlled by a joystick but also the voice control system to be prepared for the severely disabled. This voice control system responds to the result of voice recognition, state of system or warning of dangers with voices, which has real time response and 95.3% recognition ratio and satisfactory synthesis voice Quality Therefore this system is able to provide independency in driving and the disabled's daily life.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.523-529
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• 2021

In this paper, we designed and developed an Emotional Speech Synthesis Markup Language (SSML) processor. Multi-speaker emotional speech synthesis technology that can express multiple voice colors and emotional expressions have been developed, and we designed Emotional SSML by extending SSML for multiple voice colors and emotional expressions. The Emotional SSML processor has a graphic user interface and consists of following four components. First, a multi-speaker emotional text editor that can easily mark specific voice colors and emotions on desired positions. Second, an Emotional SSML document generator that creates an Emotional SSML document automatically from the result of the multi-speaker emotional text editor. Third, an Emotional SSML parser that parses the Emotional SSML document. Last, a sequencer to control a multi-speaker and emotional Text-to-Speech (TTS) engine based on the result of the Emotional SSML parser. Based on SSML which is a programming language and platform independent open standard, the Emotional SSML processor can easily integrate with various speech synthesis engines and facilitates the development of multi-speaker emotional text-to-speech applications.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.15-23
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• 2001

This paper presents a voice conversion technique that modifies the utterance of a source speaker as if it were spoken by a target speaker. Feature parameter conversion methods to perform the transformation of vocal tract and prosodic characteristics between the source and target speakers are described. The transformation of vocal tract characteristics is achieved by modifying the LPC cepstral coefficients using Linear Multivariate Regression (LMR). Prosodic transformation is done by changing the average pitch period between speakers, and it is applied to the residual signal using the LP-PSOLA scheme. Experimental results show that transformed speech by LMR and LP-PSOLA synthesis method contains much characteristics of the target speaker.

• Journal of Digital Convergence
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• v.13 no.7
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• pp.177-182
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• 2015

In a MPC(Multi-Pulse Coding) using excitation source of voiced and unvoiced, it would be a distortion of voice waveform. This is caused by normalization of synthesis speech waveform of voiced in the process of restoration. To solve this problem, this paper present APC-MPC of amplitude-position compensation in a multi-pulses each pitch interval in order to reduce distortion of synthesis waveform. Also, I was implemented that the APC-MPC in coding system. And I evaluate the SNRseg of APC-MPC in 8kbps coding condition of convergence system. As a result, SNRseg of APC-MPC was 13.9dB for female voice and 14.3dB for male voice respectively. And so, I expect to be able to this method for cellular phone and smart phone using excitation source of low bit rate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.133-141
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• 2005

The previous research has been mainly focused on individual technology of voice recognition, voice synthesis, translation, and bone transmission technical. Recently, commercial models have been produced using aforementioned technologies. In this research, a new automated translation support system concept has been proposed by combining established technology of bone transmission and wireless system. The proposed system has following three major components. First, the hybrid system consist of headset, bone transmission and other technologies will recognize user's voice. Second, computer recognized voice (using small server attached to the user) of the user will be converted into digital signal. Then it will be translated into other user's language by translation algorithm. Third, the translated language will be wirelessly transmitted to the other party. The transmitted signal will be converted into voice in the other party's computer using the hybrid system. This hybrid system will transmit the clear message regardless of the noise level in the environment or user's hearing ability. By using the network technology, communication between users can also be clearly transmitted despite the distance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.977-982
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• 2004

In this paper, I propose a new method of Multi-Pulse Coding(IP-MPC) use individual pitch pulses in order to accommodate the changes in each pitch interval and reduce pitch errors. The extraction rate of individual pitch pulses was 85% for female voice and 96% for male voice respectively. 1 evaluate the MPC by using pitch information of autocorrelation method and the IP-MPC by using individual pitch pulses. As a result, I knew that synthesis speech of the IP-MPC was better in speech quality than synthesis speech of the MPC.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.59-64
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• 2006

In this paper, 1 propose a new method of Multi-Pulse Coding(IP-MPC) use individual pitch pulses in order to accommodate the changes in each pitch interval and reduce pitch errors. The extraction rate of individual pitch pulses was $85\%$ for female voice and $96\%$ for male voice respectively, 1 evaluate the MPC by using pitch information of autocorrelation method and the IP-MPC by using individual pitch pulses. As a result, 1 knew that synthesis speech of the IP-MPC was better in speech quality than synthesis speech of the MPC.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.2
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• pp.182-187
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• 2014

We propose nonlinear GMM-based transformation functions in an attempt to deal with the over-smoothing effects of linear transformation for voice processing. The proposed methods adopt RBF networks as a local transformation function to overcome the drawbacks of global nonlinear transformation functions. In order to obtain high-quality modifications of speech signals, our voice conversion is implemented using the Harmonic plus Noise Model analysis/synthesis framework. Experimental results are reported on the English corpus, MOCHA-TIMIT.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.249-255
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• 2016

Since the elderly voices include a lot of noise caused by physiological changes in respiration, phonation, and resonance, the performance of the convergence health-care equipments such as speech recognition, synthesis, analysis program done by elderly voice is deteriorated. Therefore it is necessary to develop researches to operate health-care instruments with elderly voices. In this study, a voice activity detection using a symmetric higher-order differential energy function (SHODEO) was developed and was compared with auto-correlation function(ACF) and the average magnitude difference function(AMDF). It was confirmed to have a better performance than other methods in the voice interval detection. The voice activity detection will be applied to a voice interface for the elderly to improve the accessibility of the smart devices.

• Phonetics and Speech Sciences
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• v.12 no.3
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• pp.55-63
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• 2020

Voice conversion can be applied to various voice processing applications. It can also play an important role in data augmentation for speech recognition. The conventional method uses the architecture of voice conversion with speech synthesis, with Mel filter bank as the main parameter. Mel filter bank is well-suited for quick computation of neural networks but cannot be converted into a high-quality waveform without the aid of a vocoder. Further, it is not effective in terms of obtaining data for speech recognition. In this paper, we focus on performing voice-to-voice conversion using only the raw spectrum. We propose a deep learning model based on the transformer network, which quickly learns the voice conversion properties using an attention mechanism between source and target spectral components. The experiments were performed on TIDIGITS data, a series of numbers spoken by an English speaker. The conversion voices were evaluated for naturalness and similarity using mean opinion score (MOS) obtained from 30 participants. Our final results yielded 3.52±0.22 for naturalness and 3.89±0.19 for similarity.