• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.218-229
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• 2011

Companding algorithms have been used to enhance speech recognition in noise for cochlea implant users. The efficiency of using companding for digital hearing aid users is not yet validated. The purpose of this study is to evaluate the performance of the companding for digital hearing aid users in the various hearing loss cases. Using HeLPS, a hearing loss simulator, two different sensorinerual hearing loss conditions were simulated; mild gently sloping hearing loss(HL1) and moderate to steeply sloping hearing loss(HL2). In addition, a non-linear compression was simulated to compensate for hearing loss using national acoustic laboratories-non-linear version 1(NAL-NL1) in HeLPS. In companding, the following four different companding strategies were used changing Q values(q1, q2) of pre-filter(F filter) and post filter(G filter). Firstly, five IEEE sentences which were presented with speech-shaped noise at different SNRs(0, 5, 10, 15 dB) were processed by the companding. Secondly, the processed signals were applied to HeLPS. For comparison, signals which were not processed by companding were also applied to HeLPS. For the processed signals, log-likelihood ratio(LLR) and cepstral distance(CEP) were measured for evaluation of speech quality. Also, fourteen normal hearing listeners performed speech reception threshold(SRT) test for evaluation of speech intelligibility. As a result of this study, the processed signals with the companding and NAL-NL1 have performed better than that with only NAL-NL1 in the sensorineural hearing loss conditions. Moreover, the higher ratio of Q values showed better scores in LLR and CEP. In the SRT test, the processed signals with companding(SRT = -13.33 dB SPL) showed significantly better speech perception in noise than those processed using only NAL-NL1(SRT = -11.56 dB SPL).

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1322-1325
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• 1987

In this study, the excitation pattern, which can be sensated by a man having hearing loss due to the damage of inner ear, is extracted, and the procedure of the auditory speech signal processing is simulated with the computer. Therefore, the excitation pattern is extracted by the neural tuning model satisfying the physiological characteristic of the inner ear and by the infor.ation extracted from speech signal. The firing pattern is also extracted by inputting this excitation pattern to the auditory neural model. With this extracted firing pattern, the possibility that the patient can sensate the speech signal is studied by the computer simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.4
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• pp.159-167
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• 1985

It was demonstrated that the glottal waves are different depending on a kind of vowels in deriving the glottal waves directly from Korean vowels/a, e, I, o, u/ w, ch are recorded by a male speaker. After resynthesizing vowels with five simulated glottal waves, the effects of glottal wave shape on the speech synthesis were compared with in terms of waveform. Some changes could be seen in the waveforms of the synthetic vowels with the variation of the shape, opening time and closing time, therefore it was confirmed that in the speech sysnthesis, the glottal wave shape is an important factor in the improvement of the speech quality.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1E
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• pp.20-28
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• 1996

Speech sounds were practically applied to a cochlear filter which was simulated by an electrical transmission line. The amplitude of the basilar membrane displacement was calculated along the length of the cochlea in temporal response. And the envelope of the amplitude according to the length was arranged for each discrete time interval. The resulting time response of the speech sound was then displayed as a color image. Five vowels such as a, e, I, o, u were applied and their results were compared. The whole procedure of the visualization method of the speech sound using the cochlear filter is described in detail. The filter model response to voice is visualized by passing the voice through the cochlear filter model.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1
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• pp.58-68
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• 2005

In this paper, we propose a scheme to improve discriminative property in the feature compensation method for robust speech recognition under noisy environments. The estimation of noisy speech model used in existing feature compensation methods do not guarantee the computation of posterior probabilities which discriminate reliably among the Gaussian components. Estimation of Posterior probabilities is a crucial step in determining the discriminative factor of the Gaussian models, which in turn determines the intelligibility of the restored speech signals. The proposed scheme employs minimum classification error (MCE) training for estimating the parameters of the noisy speech model. For applying the MCE training, we propose to identify and determine the 'competing components' that are expected to affect the discriminative ability. The proposed method is applied to feature compensation based on parallel combined mixture model (PCMM). The performance is examined over Aurora 2.0 database and over the speech recorded inside a car during real driving conditions. The experimental results show improved recognition performance in both simulated environments and real-life conditions. The result verifies the effectiveness of the proposed scheme for increasing the performance of robust speech recognition systems.

• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.438-445
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• 2013

In this paper, we propose an enhancement of Japanese acoustic model which is trained with Korean speech database by using several combination strategies. We describe the strategies for training more than two language combination, which are Cross-Language Transfer, Cross-Language Adaptation, and Data Pooling Approach. We simulated those strategies and found a proper method for our current Japanese database. Existing combination strategies are generally verified for under-resourced Language environments, but when the speech database is not fully under-resourced, those strategies have been confirmed inappropriate. We made tyied-list with only object-language on Data Pooling Approach training process. As the result, we found the ERR of the acoustic model to be 12.8 %.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.703-709
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• 2019

This paper proposes speech recognition systems employing Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures combined with Hidden Markov Moldel (HMM) to effectively recognize the speech of VeloPharyngeal Insufficiency (VPI) patients, and compares the recognition performance of the systems to the Gaussian Mixture Model (GMM-HMM) and fully-connected Deep Neural Network (DNNHMM) based speech recognition systems. In this paper, the initial model is trained using normal speakers' speech and simulated VPI speech is used for generating a prior model for speaker adaptation. For VPI speaker adaptation, selected layers are trained in the CNN-HMM based model, and dropout regulatory technique is applied in the LSTM-HMM based model, showing 3.68 % improvement in recognition accuracy. The experimental results demonstrate that the proposed LSTM-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM and fully-connected DNN-HMM system.

• The Journal of the Acoustical Society of Korea
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• v.23 no.6
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• pp.473-480
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• 2004

In this paper we propose an effective feature compensation scheme based on the speech model in order to achieve robust speech recognition. The proposed feature compensation method is based on parallel combined mixture model (PCMM). The previous PCMM works require a highly sophisticated procedure for estimation of the combined mixture model in order to reflect the time-varying noisy conditions at every utterance. The proposed schemes can cope with the time-varying background noise by employing the interpolation method of the multiple mixture models. We apply the‘data-driven’method to PCMM tot move reliable model combination and introduce a frame-synched version for estimation of environments posteriori. In order to reduce the computational complexity due to multiple models, we propose a technique for mixture sharing. The statistically similar Gaussian components are selected and the smoothed versions are generated for sharing. The performance is examined over Aurora 2.0 and speech corpus recorded while car-driving. The experimental results indicate that the proposed schemes are effective in realizing robust speech recognition and reducing the computational complexities under both simulated environments and real-life conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.649-655
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• 2022

In this paper, a determinant-based two-channel noise reduction method which utilizes speech presence probability (SPP) is proposed. The proposed method improves noise reduction performance from the conventional determinant-based two-channel noise reduction method in [7] by applying SPP to the Wiener filter gain. Consequently, the proposed method adaptively controls the amount of noise reduction depending on the SPP. For performance evaluation, the segmental signal-to-noise ratio (SNR), the perceptual evaluation of speech quality, the short time objective intelligibility, and the log spectral distance were measured in the simulated noisy environments considered various types of noise, reverberation, SNR, and the direction and number of noise sources. The experimental results presented that determinant-based methods outperform phase difference-based methods in most cases. In particular, the proposed method achieved the best noise reduction performance maintaining minimum speech distortion.

• The Journal of the Acoustical Society of Korea
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• v.33 no.1
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• pp.54-59
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• 2014

Identification of RTF (Relative Transfer Function) between sensors is essential to multichannel speech enhancement system. In this paper, we present an approach for estimating the relative transfer function of speech signal. This method adapts a CASA (Computational Auditory Scene Analysis) technique to the conventional OM-LSA (Optimally-Modified Log-Spectral Amplitude) based approach. Evaluation of the proposed approach is performed under simulated stationary and nonstationary WGN (White Gaussian Noise). Experimental results confirm advantages of the proposed approach.