• MALSORI
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• no.61
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• pp.63-74
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• 2007

Discrimination of music and speech from the multimedia signal is an important task in audio coding and broadcast monitoring systems. This paper deals with the problem of feature parameter extraction for discrimination of music and speech. The wavelet transform is a multi-resolution analysis method that is useful for analysis of temporal and spectral properties of non-stationary signals such as speech and audio signals. We propose new feature parameters extracted from the wavelet transformed signal for discrimination of music and speech. First, wavelet coefficients are obtained on the frame-by-frame basis. The analysis frame size is set to 20 ms. A parameter $E_{sum}$ is then defined by adding the difference of magnitude between adjacent wavelet coefficients in each scale. The maximum and minimum values of $E_{sum}$ for period of 2 seconds, which corresponds to the discrimination duration, are used as feature parameters for discrimination of music and speech. To evaluate the performance of the proposed feature parameters for music and speech discrimination, the accuracy of music and speech discrimination is measured for various types of music and speech signals. In the experiment every 2-second data is discriminated as music or speech, and about 93% of music and speech segments have been successfully detected.

• Phonetics and Speech Sciences
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• v.9 no.4
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• pp.91-97
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• 2017

This study categorizes several voice characteristics by subjective listening assessment, and investigates correlation between voice characteristics and speech feature parameters. A model was developed to classify voice characteristics into the defined categories using SVM algorithm. To do this, we extracted various speech feature parameters from speech database for men in their 20s, and derived statistically significant parameters correlated with voice characteristics through ANOVA analysis. Then, these derived parameters were applied to the proposed SVM model. The experimental results showed that it is possible to obtain some speech feature parameters significantly correlated with the voice characteristics, and that the proposed model achieves the classification accuracies of 88.5% on average.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.435-447
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• 1996

In this paper, we propose the method that extracts the speech feature using the hearing model through signal processing techniques. The proposed method includes the following procedure ; normalization of the short-time speech block by its maximum value, multi-resolution analysis using the discrete wavelet transformation and re-synthesize using the discrete inverse wavelet transformation, differentiation after analysis and synthesis, full wave rectification and integration. In order to verify the performance of the proposed speech feature in the speech recognition task, korean digit recognition experiments were carried out using both the DTW and the VQ-HMM. The results showed that, in the case of using DTW, the recognition rates were 99.79% and 90.33% for speaker-dependent and speaker-independent task respectively and, in the case of using VQ-HMM, the rate were 96.5% and 81.5% respectively. And it indicates that the proposed speech feature has the potential for use as a simple and efficient feature for recognition task

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.13-17
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• 2017

Existing Speech feature extracting method in speech Signal, there are incorrect recognition rates due to incorrect speech which is not clear threshold value. In this article, the modeling method for improving speech recognition performance that combines the feature extraction for speech and silence characteristics normalized to the non-speech. The proposed method is minimized the noise affect, and speech recognition model are convergence of speech signal feature extraction to each speech frame and the silence feature normalization. Also, this method create the original speech signal with energy spectrum similar to entropy, therefore speech noise effects are to receive less of the noise. the performance values are improved in signal to noise ration by the silence feature normalization. We fixed speech and non speech classification standard value in cepstrum For th Performance analysis of the method presented in this paper is showed by comparing the results with CHMM HMM, the recognition rate was improved 2.7%p in the speech dependent and advanced 0.7%p in the speech independent.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.131-140
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• 1996

In this paper, we propose the method that extracts the speech feature using the hearing model through signal precessing techniques. The proposed method includes following procedure ; normalization of the short-time speech block by its maximum value, multi-resolution analysis using the discrete wavelet transformation and re-synthesize using thediscrete inverse wavelet transformation, differentiation after analysis and synthesis, full wave rectification and integration. In order to verify the performance of the proposed speech feature in the speech recognition task, korean digita recognition experiments were carried out using both the dTW and the VQ-HMM. The results showed that, in case of using dTW, the recognition rates were 99.79% and 90.33% for speaker-dependent and speaker-independent task respectively and, in case of using VQ-HMM, the rate were 96.5% and 81.5% respectively. And it indicates that the proposed speech feature has the potentials to use as a simple and efficient feature for recognition task.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2238-2245
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• 2000

In this papr, eatue parameters, extracted using Wavelet transform for Korean isolated worked speech, are sued for speech detection and recognition feature. As a result of the speech detection, it is shown that it produces more exact detection result than eh method of using energy and zero-crossing rate on speech boundary. Also, as a result of the method with which the feature parameter of MFCC, which is applied to he recognition, it is shown that the result is equal to the result of the feature parameter of MFCC using FFT in speech recognition. So, it has been verified the usefulness of feature parameters using Wavelet transform for speech analysis and recognition.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.754-759
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• 2007

In this paper, we proposed the Bi-Modal Sensor Fusion Algorithm which is the emotional recognition method that be able to classify 4 emotions (Happy, Sad, Angry, Surprise) by using facial image and speech signal together. We extract the feature vectors from speech signal using acoustic feature without language feature and classify emotional pattern using Neural-Network. We also make the feature selection of mouth, eyes and eyebrows from facial image. and extracted feature vectors that apply to Principal Component Analysis(PCA) remakes low dimension feature vector. So we proposed method to fused into result value of emotion recognition by using facial image and speech.

• MALSORI
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• no.43
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• pp.179-188
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• 2002

Improving the recognition performance of connected digit telephone speech still remains a problem to be solved. As a basic study for it, this paper analyzes the variation of feature parameters of Korean digit telephone speech according to channel distortion. As a feature parameter for analysis and recognition MFCC is used. To analyze the effect of telephone channel distortion depending on each call, MFCCs are first obtained from the connected digit telephone speech for each phoneme included in the Korean digit. Then CMN, RTCN, and RASTA are applied to the MFCC as channel compensation techniques. Using the feature parameters of MFCC, MFCC+CMN, MFCC+RTCN, and MFCC+RASTA, variances of phonemes are analyzed and recognition experiments are done for each case. Experimental results are discussed with our findings and discussions

• 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.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.81-84
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• 2000

In this paper, a new technology for extracting the feature of the speech signal of an isolated word by the analysis on the frequency domain is proposed. This technology can be applied efficiently for the limited speech domain. In order to extract the feature of speech signal, the number of peaks is calculated and the value of the frequency for a peak is used. Then the difference between the maximum peak and the second peak is also considered to identify the meanings among the words in the limited domain. By implementing this process hierarchically, the feature of speech signal can be extracted more quickly.