• Speech Sciences
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• v.5 no.1
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• pp.63-80
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• 1999

In this paper, an adaptive method of dividing a speech signal into an initial, a medial and a final sound of the form of utterance utilized by evaluating extreme limits of short term energy and autocorrelation functions. By applying this method into speech signal composed of a consonant, a vowel and a consonant, it was divided into an initial, a medial and a final sound and its feature analysis of sample by LPC were carried out. As a result of spectrum analysis in each period, it was observed that there existed spectrum features of a consonant and a vowel in the initial and medial periods respectively and features of both in a final sound. Also, when all kinds of words were adaptively divided into 3 periods by using the proposed method, it was found that the initial sounds of the same consonant and the medial sounds of the same vowels have the same spectrum characteristics respectively, but the final sound showed different spectrum characteristics even if it had the same consonant as the initial sound.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.90-99
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• 1992

In this paper, we propose a neural network model for speech recognition. The model consists of feature extraction parts and recognition parts. The interconnection model based on ${\Delta}^2$G operator was used for frequency analysis. Two features, global feature and local feature, were extracted from this model. Recognition parts consist of global grouping stage and local grouping stage. When the input pattern was coded by slope method, the recognition rate of speakers, A and B, was 100%. When the test was performed with the data of 9 speakers, the recognition rate of 91.4% was obtained.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.195-200
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• 2004

In this paper we propose effective speech recognizer through two recognition experiments. In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition performance of HMM and SVM at training data number and investigate recognition performance of each feature parameter while changing feature space of MFCC using Independent Component Analysis(ICA) and Principal Component Analysis(PCA). As a result of experiment, recognition performance of SVM is better than 1:.um under few training data number, and feature parameter by ICA showed the highest recognition performance because of superior linear classification.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1042-1045
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• 2003

In this paper, we propose the speaker identification system that uses vowel that has speaker's characteristic. System is divided to speech feature extraction part and speaker identification part. Speech feature extraction part extracts speaker's feature. Voiced speech has the characteristic that divides speakers. For vowel extraction, formants are used in voiced speech through frequency analysis. Vowel-a that different formants is extracted in text. Pitch, formant, intensity, log area ratio, LP coefficients, cepstral coefficients are used by method to draw characteristic. The cpestral coefficients that show the best performance in speaker identification among several methods are used. Speaker identification part distinguishes speaker using Neural Network. 12 order cepstral coefficients are used learning input data. Neural Network's structure is MLP and learning algorithm is BP (Backpropagation). Hidden nodes and output nodes are incremented. The nodes in the incremental learning neural network are interconnected via weighted links and each node in a layer is generally connected to each node in the succeeding layer leaving the output node to provide output for the network. Though the vowel extract and incremental learning, the proposed system uses low learning data and reduces learning time and improves identification rate.

• MALSORI
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• no.43
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• pp.127-136
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• 2002

The speaker adaptation technique is generally used to reduce the speaker difference in speech recognition. In this work, we focus on the features fitted to a linear regression-based speaker adaptation. These are obtained by feature transformation based on independent component analysis (ICA), and the transformation matrix is learned from a speaker independent training data. When the amount of data is small, however, it is necessary to adjust the ICA-based transformation matrix estimated from a new speaker utterance. To cope with this problem, we propose a smoothing method: through a linear interpolation between the speaker-independent (SI) feature transformation matrix and the speaker-dependent (SD) feature transformation matrix. We observed that the proposed technique is effective to adaptation performance.

• Phonetics and Speech Sciences
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• v.2 no.4
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• pp.239-244
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• 2010

The purpose of this study is to investigate two phonological error patterns on coronal feature of children with functional articulation disorders and to compare them with those of general children. We tested 120 children with functional articulation disorders and 100 general children from 2~4 years of age with 'Assessment of Phonology & Articulation for Chidren(APAC)'. The results were as follows: (1) 37 disordered children substituted [+coronal] consonants for [-coronal] consonants (fronting of velars) and 9 disordered children substituted [-coronal] consonants for [+coronal] consonants (backing to velars). (2) Theses two phonological patterns were affected by the articulatory place of following phoneme. (3) The fronting pattern of children with articulation disorders was similar with that of general children, but their backing pattern was different with that of general children. These results show the clinical usefulness of coronal feature in phonological pattern analysis, the need of articulatory assessment with various phonetic context, and the importance of error contexts in clinical judgment.

• Speech Sciences
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• v.3
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• pp.156-166
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• 1998

In this paper, I would like to propose a dividing method by estimating the inflection points and the average magnitude energy in speech signals. The method proposed in this paper gave not only a satisfactory solution for the problems on dividing method by zero-crossing rate, but could estimate the feature of the transient period after dividing the starting point and transient period in speech signals before steady state. In the results of the experiment carried out with monosyllabic speech, it was found that even through speech samples indicated in D.C. level, the staring and ending point of the speech signals were exactly divided by the method. In addition to the results, I could compare with the features, such as the length of transient period, the short term energy, the frequency characteristics, in each speech signal.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.156-163
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.1-6
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• 2005

In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.