• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.779-782
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• 2008

It is important to detect speech active interval from Noisy Speech in speech communication and speech recognition. In this research, we propose characteristic parameter with combining spectral Entropy for detect speech active interval in Noisy Speech, and compare performance of speech active interval based on energy. The results shows that analysis using proposed characteristic parameter is higher performance the others in noisy environment.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.2
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• pp.60-65
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• 2002

In this paper, we propose GFCC(gammatone filter frequency cepstrum coefficient) parameter which was based on the auditory characteristic for accomplishing better speech recognition rate. And it is performed the experiment of speech recognition for isolated word acquired from telephone network. For the purpose of comparing GFCC parameter with other parameter, the experiment of speech recognition are carried out using MFCC and LPCC parameter. Also, for each parameter, we are implemented CMS(cepstral mean subtraction)which was applied or not in order to compensate channel distortion in telephone network. Accordingly, we found that the recognition rate using GFCC parameter is better than other parameter in the experimental result.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.269-272
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• 2001

In speech recognition technology, the utterance of every talker have special resonant frequency according to shape of talker's lip and to the motion of tongue. And utterances are different according to each talker. Accordingly, we need the superior moth-od of speech feature parameter extraction which reflect talker's characteristic well. This paper suggests the modified-MfCC combined existing MFCC with gammatone filter. We experimented with speech data from telephone and then we obtained results of enhanced speech recognition rate which is higher than that of the other methods.

• Speech Sciences
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• v.1
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• pp.285-293
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• 1997

This paper was constructed to investigate strange attractor in considering speech which is regarded as chaos in that the random signal appears in the deterministic raising system. This paper searches for the delay time from AR model power spectrum for constructing fit attractor for speech signal. As a result of applying Taken's embedding theory to the delay time, an exact correlation dimension solution is obtained. As a result of this consideration of speech, it is found that it has more speaker recognition characteristic parameter, and gains a large speaker discrimination recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1596-1603
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• 2007

In this paper, we experimented by three kind of method for feature parameter, training method and recognition algorithm of most suitable for speech recognition system and considered. We decided speech recognition system of most suitable through two kind of experiment after we make speech recognizer. First, we did an experiment about three kind of feature parameter to evaluate recognition performance of it in speech recognizer using existent MFCC and MFCC new feature parameter that change characteristic space using PCA and ICA. Second, we experimented recognition performance or HMM, SVM and RVM by studying data number. By an experiment until now, feature parameter by ICA showed performance improvement of average 1.5% than MFCC by high linear discrimination from characteristic space. RVM showed performance improvement of maximum 3.25% than HMM in an experiment by decrease of studying data. As such result, effective method for speech recognition system to propose in this paper derives feature parameters using ICA and un recognition using RVM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1673-1678
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• 2016

In the area of the speech recognition processing, background noises are caused the incorrect response to the speech input, therefore the speech recognition rates are decreased by the background noises. Accordingly, a more high level noise processing techniques are required since these kinds of noise countermeasures are not simple. Therefore, this paper proposes an algorithm to distinguish between the stationary background noises or non-stationary background noises and the speech signal having short time duration in the noisy environments. The proposed algorithm uses the characteristic parameter of the improved speech signal as an important measure in order to distinguish different types of the background noises and the speech signals. Next, this algorithm estimates various kinds of the background noises using a multi-layer perceptron neural network. In this experiment, it was experimentally clear the estimation of the background noises and the speech signals.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.57-60
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• 2001

This paper reports on speaker-Recognition of context independence-speaker recognition in the field of the speech recognition. It is important to select the parameter reflecting the characteristic of each single person because speaker-recognition is to identify who speaks in the database. We used Mel Frequency Cesptrum Coefficient and Vector Quantization to identify in this paper. Specially, it considered to find characteristic-vector of the speaker in different from known method; this paper used the characteristic-vector which is selected in MFCC Parameter Space. Also, this paper compared the recognition rate according to size of codebook from this database and the time needed for operation with the existing one. The results is more improved $3\sim4\%$ for recognition rate than established Vector Quantization Algorithm.

• Speech Sciences
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• v.10 no.2
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• pp.85-95
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• 2003

In speech signal processing, it is very important to detect the pitch exactly in speech recognition, synthesis and analysis. If we exactly pitch detect in speech signal, in the analysis, we can use the pitch to obtain properly the vocal tract parameter. It can be used to easily change or to maintain the naturalness and intelligibility of quality in speech synthesis and to eliminate the personality for speaker-independence in speech recognition. In this paper, we proposed a new pitch detection algorithm. First, positive center clipping is process by using the incline of speech in order to emphasize pitch period with a glottal component of removed vocal tract characteristic in time domain. And rough formant envelope is computed through peak-fitting spectrum of original speech signal infrequence domain. Using the roughed formant envelope, obtain the smoothed formant envelope through calculate the linear interpolation. As well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. Inverse fast fourier transform (IFFT) compute this flattened harmonics. After all, we obtain Residual signal which is removed vocal tract element. The performance was compared with LPC and Cepstrum, ACF. Owing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.175-179
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• 1993

A Study on the speech processing and coding strategy for cochlear implants have been developed to create a speech signal processing system which extracts stimulus parameter including formants, pitch, amplitude information. In this study we have presented the method which extracts characteristic information of speech signal and adapt patients with hearing handicap.

• Speech Sciences
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• v.15 no.3
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• pp.7-15
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• 2008

This paper proposes the method to extract new feature vectors using the difference between the cepstrum for static characteristics and delta cepstrum for dynamic characteristics in speaker recognition (SR). The difference vector (DV) which it proposes from this paper is containing the static and the dynamic characteristics simultaneously at the intermediate characteristic vector which uses the deference between the static and the dynamic characteristics and as the characteristic vector which is new there is a possibility of doing. Compared to the conventional method, the proposed method can achieve new feature vector without increasing of new parameter, but only need the calculation process for the difference between the cepstrum and delta cepstrum. Experimental results show that the proposed method has a good performance more than 2.03%, on average, compared with conventional method in speaker identification (SI).