• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.334-337
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• 2005

The speech intelligibility is one of the most important factors in communication system. The speech intelligibility is related with speech to noise ratio. To enhance the speech to noise ratio, background noise reduction techniques are being developed. As a part of solution to noise reduction, this paper introduces directional microphone using beamforming method and speech filtering method. The directional microphone narrows the spatial range of processing signal into the direction of the target speech signal. The noise signal located in the same direction with speech still remains in the processing signal. To sort this mixed signal into speech and noise, as a following step, a speech-filtering method is applied to pick up only the speech signal from the processed signal. The speech filtering method is based on the characteristics of speech signal itself. The combined directional microphone and speech filtering method gives enhanced performance to speech intelligibility in communication system.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.93-101
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• 1996

In this paper, we implement the system for speech processing to provide the subscribers who are using the telephone network with better speech services. We develop the specified board which is processing speech signal and devise the system which carries out storing and replaying the speech signal under the condition that one master board controls multiple DSP(Digital Signal Processing) boards using VME bus. We use CPU30 board as a maste board and develop SPM(Signal Processing Module) board as a DSP board and then evaluate performance of the system.

• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.33-41
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• 1995

We proposed the approach method of neural network for signal processing, especially speech signal processing and reviewed the algorithms for several neural networks which are used for many alppication field in speech processing. Finally, investigated the trends in neural network method through 3 conference jounal and the ASK jounal in 1994.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.121-128
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• 2005

On the research field for speech signal, neural network mainly uses for the category classification in speech recognition and applies to signal processing. Accordingly, this paper proposes a noise reduction system using a time-delay neural network, which implements the mapping from the space of speech signal degraded by noise to the space of clean speech signal. It is confirmed that this method is effective for speech degraded not only by white noise but also by colored noise using the noise reduction system, which restores the amplitude component of fast Fourier transform.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.178-183
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• 2006

As a lot of researches on the speech signal processing are performed due to the recent rapid development of the information-communication technology. the pitch period is used as an important element to various speech signal application fields such as the speech recognition. speaker identification. speech analysis. or speech synthesis. A variety of algorithms for the time and the frequency domains related with such pitch period detection have been suggested. One of the pitch detection algorithms for the time domain. AMDF (average magnitude difference function) uses distance between two valley points as the calculated pitch period. However, it has a problem that the algorithm becomes complex in selecting the valley points for the pitch period detection. Therefore, in this paper we proposed the modified AMDF(M-AMDF) algorithm which recognizes the entire minimum valley points as the pitch period of the speech signal by using the rotation transform of AMDF. In addition, a threshold is set to the beginning portion of speech so that it can be used as the selection criteria for the pitch period. Moreover the proposed algorithm is compared with the conventional ones by means of the simulation, and presents better properties than others.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.13-18
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• 1997

Each reproducing speech signal has each particular signal property, because of the processing of encoding and decoding for communications through various media. In this paper, we examine the correlation between speech signal quality and sensory pleasure for the sensory improvement of that signal. In experiments, we evaluate the quality of speech signals through various media by psychological auditory test and physical features of these signals.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.282-285
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• 2005

This paper discusses the interactive system for smart home environments. In order to realize this, the main emphasis of the paper lies on the description of the multiple signal processing on the basis of the technologies such as fingerprint recognition, video signal processing, speech recognition and synthesis. For essential modules of the interactive system, we adopted the motion detector based on the changes of brightness in pixels as well as the fingerprint identification for adapting home environments to the inhabitants. In addition, the real-time speech recognizer based on the HM-Net(Hidden Markov Network) and the speech synthesis were incorporated into the overall system for interaction between user and system. In experimental evaluation, the results showed that the proposed system was easy to use because the system was able to give special services for specific users in smart home environments, even though the performance of the speech recognizer was not better than the simulation results owing to the noisy environments.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.37-41
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• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.83-86
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• 1997

To provide ameliorated aided environment to hearing impaired listeners, background noise reduction techniques are investigated as a front-end of conventional hearing aids, and their effects are tested in a subjective manner. Several speech enhancement schemes were implemented and preference tests or normal listeners are performed to select the best possible scheme or hearing impaired listeners. Results indicated that SDT scores without the speech enhancement scheme drop more sharply as SNR decreases than those with the speech enhancement techniques. SDT scores obtained or hearing impaired listeners with hearing aids showed large variability. However, all impaired listeners preferred noise suppressed sounds to unsuppressed ones.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.748-764
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• 2019

In order to deal with the filtering delay problem of least mean square adaptive filter noise reduction algorithm and music noise problem of spectral subtraction algorithm during the speech signal processing, we combine these two algorithms and propose one novel noise reduction method, showing a strong performance on par or even better than state of the art methods. We first use the least mean square algorithm to reduce the average intensity of noise, and then add spectral subtraction algorithm to reduce remaining noise again. Experiments prove that using the spectral subtraction again after the least mean square adaptive filter algorithm overcomes shortcomings which come from the former two algorithms. Also the novel method increases the signal-to-noise ratio of original speech data and improves the final noise reduction performance.