• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.359-364
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• 2004

In this paper, we implemented real-time speaker undependent speech recognizer that is robust in noise environment using DSP(Digital Signal Processor). Implemented system is composed of TMS320C32 that is floating-point DSP of Texas Instrument Inc. and CODEC for real-time speech input. Speech feature parameter of the speech recognizer used robust feature parameter in noise environment that is transformed feature space of MFCC(met frequency cepstral coefficient) using ICA(Independent Component Analysis) on behalf of MFCC. In recognition result in noise environment, we hew that recognition performance of ICA feature parameter is superior than that of MFCC.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1
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• pp.28-34
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• 2003

Recent researches have been focusing on jointly using lip motions (i.e. visual speech) and speech for reliable speech recognitions in noisy environments. This paper also deals with the method of combining the result of the visual speech recognizer and that of the conventional speech recognizer through putting weights on each result: the paper proposes the method of determining proper weights for each result and, in particular, the weights are autonomously determined, depending on the amounts of noise in the speech and the image quality. Simulation results show that combining the audio and visual recognition by the proposed method provides the recognition performance of 84% even in severely noisy environments. It is also shown that in the presence of blur in images, the newly proposed weighting method, which takes the blur into account as well, yields better performance than the other methods.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2E
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• pp.32-37
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• 1997

This paper proposes a isolated speech recognition method of Korean digits using a TDNN(Time Delay Neural Network) which is able to recognizc time-varying speech properties. We also make an investigation of effect on network parameter of TDNN ; hidden layers and time-delays. TDNNs in our experiments consist of 2 and 3 hidden layers and have several time-delays. From experiment result, TDNN structure which has 2 hidden-layers, gives a good result for speech recognition of Korean digits. Mis-recognition by time-delays can be improved by changing TDNN structures and mis-recognition separated from time-delays can be improved by changing input patterns.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.137-142
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• 2010

Although multi-model speech recognizer has been shown to be quite successful in noisy speech recognition, the results were based on general speech front-ends which do not take into account noise adaptation techniques. In this paper, for the accurate evaluation of the multi-model based speech recognizer, we adopted a quite noise-robust speech front-end, AFE, which was proposed by the ETSI for the noisy DSR environment. For the performance comparison, the MTR which is known to give good results in the DSR environment has been used. Also, we modified the structure of the multi-model based speech recognizer to improve the recognition performance. N reference HMMs which are most similar to the input noisy speech are used as the acoustic models for recognition to cope with the errors in the selection of the reference HMMs and the noise signal variability. In addition, multiple SNR levels are used to train each of the reference HMMs to improve the robustness of the acoustic models. From the experimental results on the Aurora 2 databases, we could see better recognition rates using the modified multi-model based speech recognizer compared with the previous method.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1E
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• pp.20-24
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• 2006

In this paper, we focused on the real time implementation of a speech recognition system with medium size of vocabulary considering its application to a mobile phone. First, we developed the PC based variable vocabulary word recognizer having the size of program memory and total acoustic models as small as possible. To reduce the memory size of acoustic models, linear discriminant analysis and phonetic tied mixture were applied in the feature selection process and training HMMs, respectively. In addition, state based Gaussian selection method with the real time cepstral normalization was used for reduction of computational load and robust recognition. Then, we verified the real-time operation of the implemented recognition system on the TMS320C6201 EVM board. The implemented recognition system uses memory size of about 610 kbytes including both program memory and data memory. The recognition rate was 95.86% for ETRI 445DB, and 96.4%, 97.92%, 87.04% for three kinds of name databases collected through the mobile phones.

• MALSORI
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• no.48
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• pp.155-173
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• 2003

This paper proposes a noise reduction-based speech detection method under telephone channel environments. We adopt the AURORA front-end noise reduction algorithm based on the two-stage mel-warped Wiener filter approach as a preprocessor for the frequency domain speech detector. The speech detector utilizes mel filter-bank based useful band energies as its feature parameters. The preprocessor firstly removes the adverse noise components on the incoming noisy speech signals and the speech detector at the next stage detects proper speech regions for the noise-reduced speech signals. Experimental results show that the proposed noise reduction-based speech detection method is very effective in improving not only the performance of the speech detector but also that of the subsequent speech recognizer.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.115-122
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• 2002

This paper proposes a robust speech recognition algorithm based on the wavelet filter banks. Since the proposed algorithm adopts a multiple band decision-making scheme, it performs robustness for noise as the presence of noisy severely degrades the performance of speech recognition system. For evaluating the performance of the proposed scheme, we compared it with the conventional speech recognizer based on the VQ for the 10-isolated korean digits with car noise. Here, the proposed method showed more 9~27% improvement of the recognition rate than the conventional VQ algorithm for the various car noisy environments.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.183-186
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• 2002

The speech detection is one of the important problems in real-time speech recognition. The accurate detection of speech boundaries is crucial to the performance of speech recognizer. In this paper, we propose a speech detector based on Mel-band selection through training. In order to show the excellence of the proposed algorithm, we compare it with a conventional one, so called, EPD-VAA (EndPoint Detector based on Voice Activity Detection). The proposed speech detector is trained in order to better extract keyword speech than other speech. EPD-VAA usually works well in high SNR but it doesn't work well any more in low SNR. But the proposed algorithm pre-selects useful bands through keyword training and decides the speech boundary according to the energy level of the sub-bands that is previously selected. The experimental result shows that the proposed algorithm outperforms the EPD-VAA.

• Speech Sciences
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• v.14 no.2
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• pp.115-126
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• 2007

There have been many research efforts to improve the performance of the speech recognizer in noisy conditions. Among them, the model compensation method and the speech enhancement approach have been used widely. In this paper, we propose to combine the two different approaches to further enhance the recognition rates in the noisy speech recognition. For the speech enhancement, the minimum mean square error-short time spectral amplitude (MMSE-STSA) has been adopted and the parallel model combination (PMC) and Jacobian adaptation (JA) have been used as the model compensation approaches. From the experimental results, we could find that the hybrid approach that applies the model compensation methods to the enhanced speech produce better results than just using only one of the two approaches.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.681-686
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• 2012

This paper studied the speech parameters less affected by the human emotion for the development of the robust emotional speech recognition system. For this purpose, the effect of emotion on the speech recognition system and robust speech parameters of speech recognition system were studied using speech database containing various emotions. In this study, mel-cepstral coefficient, delta-cepstral coefficient, RASTA mel-cepstral coefficient, root-cepstral coefficient, PLP coefficient and frequency warped mel-cepstral coefficient in the vocal tract length normalization method were used as feature parameters. And CMS (Cepstral Mean Subtraction) and SBR(Signal Bias Removal) method were used as a signal bias removal technique. Experimental results showed that the HMM based speaker independent word recognizer using frequency warped RASTA mel-cepstral coefficient in the vocal tract length normalized method, its derivatives and CMS as a signal bias removal showed the best performance.