• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.692-697
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• 2000

멀티미디어 개인 휴대 통신(MPCS)에서 다중 클래스호에 대한 효율적인 채널할당은 매우 중요하다고 할 수 있다. 본 논문에서는 Image/Voice/Data 호에 대하여 가변적 보호 채널을 할당하는 새로운 방식을 제안하였다. 이러한 방식은 3차원 상태 천이도로 모델링 되며 보호 채널의 크기를 가변적으로 조절함으로써 보다 융통성있는 서비스를 제공하게 되며, 또한 수학적 분석과 시뮬레이션을 통해 비교분석을 수행하였다.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.83-87
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• 1995

The implementation of the voice dodec is imjplemented by using TMS320C30, which is the floating point DSP chip from Texas Instrument. QCELP (Qualcomm Code Excited Linear Prediction) is used to encode and decode the voice. The QCELP code is implemented by the TMS320C30 C-dode. The DSP board is controlled by the PC. The PC program tranfors the voice file from and to the DSP board, which is also implemented by C-code. The voice is encoded by the DSP board and the encoded data is transferred to PC to be stored as a file. To hear the voice. the voice data file is sent to DSP board and decoded to synthesize audible voice. Two flags are used by both programs to notify the status of the operation. By checking the flags, DSP and PC decides when the voice data is transferred between them.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.175-179
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• 2011

In this paper, we propose the MA(Model Adaption) HMM that to use speech enhancement and feature compensation. Normally voice reference data is not consider for real noise data. This method is not to use estimated noise but we use real life environment noise data. And we applied this contaminated data for recognition reference model that suitable for noise environment. MAHMM is combined with surround noise when generating reference patten. We improved voice recognition rate at mobile environment to use MAHMM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.4
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• pp.360-368
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• 1989

In this paper, the performance of an integrated voice/data cut-through switching network is studied. We first derive cut-through probabilities of voice and data packets at intermediate nodes. Then, the Laplace transform for the network delay is obtained. According to numerical results, the performance of cut-through switching is superior to that of packet switching for integrated voice/data networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.651-659
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• 2000

In this paper, a computer simulation approach is proposed to analyze the performance of reverse traffic channels in a base station serving voice and data calls in CDMA cellular systems. It is assumed that multiple traffic channels simultaneously serve a data call and a voice call is served by one traffic channel in a base station. The numerical examples will be presented to derive the capacity of traffic channels from the parameters such as the arrival rates and mean durations of voice and data calls, the soft handoff area ratio, etc..

• Speech Sciences
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• v.8 no.3
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• pp.43-50
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• 2001

This paper deals with voice similarities between sisters who are supposed to have common physiological characteristics from a single biological mother. Nine pairs of sisters who are believed to have similar voices participated in this experiment. The speech samples obtained from one pair of sisters were eliminated in the analysis because their perceptual score was relatively low. The words were measured in both isolation and context, and the subjects were asked to read the text five times with about three seconds of interval between readings. Recordings were made at natural speed in a quiet room. The data were analyzed in pitch and formant frequencies using CSL (Computerized Speech Lab) and PCQuirer. It was found that data of the initial vowels are much more similar and homogeneous than those of vowels in other positions. The acoustic data showed that voice similarities are strikingly high in both pitch and formant frequencies. It is assumed that statistical data obtained from this experiment can be used as a guideline for modelling speaker identification and speaker verification.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.209-214
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• 2020

VoIP technology has been widely used for exchanging voice or image data through IP networks. VoIP technology, often called Internet Telephony, sends and receives voice data over the RTP protocol during the session. However, there is an exposition risk in the voice data in VoIP using the RTP protocol, where the RTP protocol does not have a specification for encryption of the original data. We implement programs that can extract meaningful information from the user's dialogue. The meaningful information means the information that the program user wants to obtain. In order to do that, our implementation has two parts. One is the client part, which inputs the keyword of the information that the user wants to obtain, and the other is the server part, which sniffs and performs the speech recognition process. We use the Google Speech API from Google Cloud, which uses machine learning in the speech recognition process. Finally, we discuss the usability and the limitations of the implementation with the example.

• Asia Pacific Journal of Business Review
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• v.6 no.1
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• pp.1-19
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• 2021

This research is based on the relevant research literature on the voice behavior, this study examines the influence of trust in leader on voice behavior. And this study examines the role of psychological safety as a mediating between trust in leader and voice behavior. This study uses SPSS for data analysis. The results of the study are as follows: This study explains the impact of trust in leader on psychological safety, and explains the impact of trust in leader on organization members' voice behavior, as well as the impact of psychological safety on organization members' voice behavior. And this study also explains the influence of psychological safety as an intermediary effect on trust in leader and voice behavior. Overall, the higher the level of organization members' trust in leader, the more they will increase the level of psychological safety of the organization members, which will prompt the organization members to make more voice behavior.

• Journal of Information Technology Services
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• v.20 no.5
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• pp.189-207
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• 2021

Voice interaction combined with artificial intelligence is poised to revolutionize human-computer interactions with the advent of virtual assistants. This paper is analyzing interactive elements of AI-based voice recognition services such as sympathy, assurance, intimacy, and trust on intention to use. The questionnaire was carried out for 284 smartphone/smart TV users in Korea. The collected data was analyzed by structural equation model analysis and bootstrapping. The key results are as follows. First, AI-based voice recognition service characteristics such as sympathy, assurance, intimacy, and trust have positive effects on interactivity with the AI-based voice recognition service. Second, the interactivity with the AI-based voice recognition service has positive effects on intention to use. Third, AI-based voice recognition service characteristics such as interactional enjoyment and intimacy have directly positive effects on intention to use. Fourth, AI-based voice recognition service characteristics such as sympathy, assurance, intimacy and trust have indirectly positive effects on intention to use the AI-based voice recognition service by mediating the effect of the interactivity with the AI-based voice recognition service. It is meaningful to investigate factors affecting the interactivity and intention to use voice recognition assistants. It has practical and academic implications.