• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.1
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• pp.71-78
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• 1984

In this paper a new method of improving the performance of ADPCM in noisy channel is proposed. The proposed method employs a robust quantizer, and transmits the information regarding the maximum step size periodically. Also, a scheme to correct most significant bit (MSB) errors is used in the receiver buffer. According to our computer simulation with real speech, the proposed ADPCM with error control yields an improvement of about 4 to 5 dB in noisy channel over the conventional ADPCM without error control.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.9-14
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• 2002

In this paper we propose a method for performance enhancement of speech recognizer under noisy conditions. The parallel combination model which is presented at the PMC method using multiple Gaussian-distributed mixtures have been adapted to the variation of each mixture. The CDHMM(continuous observation density HMM) which has multiple Gaussian distributed mixtures are combined by the proposed PMC method. Also, the EM(expectation maximization) algorithm is used for adapting the model mean parameter in order to reduce the variation of the mixture density. The result of simulation, the proposed PMC adaptation method show better performance than the conventional PMC method.

• MALSORI
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• no.66
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• pp.73-86
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• 2008

Beamforming is one of the spatial filtering techniques which extract only desired signals from noisy environments using microphone arrays. Fixed beamforming is a simple concept and easy to implement. However, it does not show good performance in real noisy conditions. As an adaptive beamforming, Frost algorithm can be a good candidate. It uses the concept of the linearly constrained minimum variance (LCMV) algorithm. The difference between the Frost and the LCMV algorithm is the error correction scheme which is very effective feature in the aspect of performance. In this paper, as quadrature mirror filtering (QMF)-based filterbank is utilized as the pre-processing of the Frost beamformning, the filter length and the learning rate of each band is optimized to improve the performance. The performance is measured by the signal-to-noise ratio (SNR) and the Bark's scale spectral distortion (BSD).

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.768-774
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• 2008

In many areas of speech signal processing such as automatic speech recognition and packet based voice communication technique, VAD (voice activity detection) plays an important role in the performance of the overall system. In this paper, we present a new feature parameter for VAD which is the product of energy of the signal and the difference of two types of entropies. For this end, we first define a Mel filter-bank based entropy and calculate its difference from the conventional entropy in frequency domain. The difference is then multiplied by the spectral energy of the signal to yield the final feature parameter which we call PEED (product of energy and entropy difference). Through experiments. we could verify that the proposed VAD parameter is more efficient than the conventional spectral entropy based parameter in various SNRs and noisy environments.

• Speech Sciences
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• v.9 no.4
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• pp.27-38
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• 2002

The purpose of this article is to present the acoustic parameters (VOT, jitter, shimmer, vF0, vAm, NHR, SPI, VTI, DVB, DSH) for consonants (/pipi/, /$p^{h}ip^{h}i$/, /p'ip'i/) and sustained vowels (/a/, /e/, /i/) produced by normal subjects and dysphonia patients at two vocal effort(normal, high) by Lombard effect using 60dB white noise. Lombard effect indicates the vocal effort increase in noisy situation. At normal vocal effort, in general the acoustic parameter values of patients are greater than normal. And in noisy situation, significant decrease of acoustic values is seen in normal compared with in dysphonia patients. The clinical implication of this finding, the vocal quality in dysphonia is not compensated by vocal effort as well as normal subjects because of the inefficiency caused by abnormal vocal fold appearance and function. And with this result, we can counsel that the voice quality can not be improved as well as the patient expect.

• Speech Sciences
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• v.15 no.1
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• pp.75-83
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• 2008

The interior of an automobile is a very noisy environment with both stationary cruising noise and the reverberated music or speech coming out from the audio system. For robust speech recognition in a car environment, it is necessary to extract a driver's voice command well by removing those background noises. Since we can handle the music and speech signals from an audio system in a car, the reverberated music and speech sounds can be removed using an acoustic echo canceller. In this paper, we implement an acoustic echo canceller with robust double-talk detection algorithm using TMS-320C6711 DSK. First we developed the echo canceller on the PC for verifying the performance of echo cancellation, then implemented it on the TMS320C6711 DSK. For processing of one speech sample with 8kHz sampling rate and 256 filter taps of the echo canceller, the implemented system used only 0.035ms and achieved the ERLE of 20.73dB.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.719-725
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• 2007

In this paper, we present an audio-visual speech recognition system for noise-robust human-computer interaction. Unlike usual speech recognition systems, our system utilizes the visual signal containing speakers' lip movements along with the acoustic signal to obtain robust speech recognition performance against environmental noise. The procedures of acoustic speech processing, visual speech processing, and audio-visual integration are described in detail. Experimental results demonstrate the constructed system significantly enhances the recognition performance in noisy circumstances compared to acoustic-only recognition by using the complementary nature of the two signals.

• Speech Sciences
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• v.9 no.4
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• pp.275-287
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• 2002

In man-machine communication by speech under the noisy environment, the quality of speech may be degraded severely for the machine to recognize correctly. Especially when the corrupting noise occupies the same band as the speech, the conventional fixed filters cannot filter out the noise effectively. In recent, to resolve such a problem adaptive noise canceller (ANC) is frequently used, which is based upon adaptive filters. The Adaptive recursive filters perform better than adaptive nonrecursive filters due to the added poles, but the stability may be severely threatened. In this paper an ANC system employing the adaptive recursive filter is proposed to enhance the speech corrupted by noise. And the stability of the adaptive recursive filter is guaranteed by employing the adaptive compensator.

• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.87-94
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• 2011

The decision directed (DD) approach is widely used to determine a priori SNR from noisy speech signals. In conventional speech enhancement systems with a DD approach, a priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a phase-dependent two-step a priori SNR estimator based on the minimum mean square error (MMSE) in the log-mel spectral domain so that we can consider both magnitude and phase information, and it can overcome the performance degradation caused by one frame delay. From the experimental results, the proposed estimator is shown to improve the output SNR of enhanced speech signals by 2.3 dB compared to the conventional DD approach-based system.

• Phonetics and Speech Sciences
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• v.2 no.4
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• pp.145-151
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• 2010

In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.