• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.3
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• pp.5-12
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• 1982

Through the use of energy ratio of special bandwidths of basic vowels, recognition of Korean basic spoken digit is performed in logical combination with a zero-crossing rate and an energy parameter. In the experiments for recognition of the digits, the speech signal of spoken digits is filtered by a lowpass filter of which the cutoff frequency is 10KHz, and then sampled at 20KHz of sampling rate, In the speech signal processing, we used four FIR digital filters, and the order of filter lengths is 61, 120, 25, 25respectively. The filters are designed by using Remetz exchange algorithm.[13],[14] As a result, the recognition rate of 92% for the three speakers is obstained.

• The Journal of the Acoustical Society of Korea
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• v.10 no.2
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• pp.23-28
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• 1991

This paper is a study on isolated word recognition by independent speaker, we propose DTW speech recognition system by modified dynamic averaging method as reference pattern. 57 city names are selected as recognition vocabulary and 2th LPC cepstrum coefficients are used as the feature parameter. In this paper, besides recognition experiment using modified dynamic averaging method as reference pattern, we perform recognition experiments using causal method, dynamic averaging method, linear averaging method and clustering method with the same data in the same conditions for comparison with it. Through the experiment result, it is proved that recogntion rate by DTW using modified dynamic averaging method is the best as 97.6 percent.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.268-272
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• 2014

This paper presents a method for speech recognition using multi-section vector-quantization (MSVQ) and time-delay recurrent neural network (TDTNN). The MSVQ generates the codebook with normalized uniform sections of voice signal, and the TDRNN performs the speech recognition using the MSVQ codebook. The TDRNN is a time-delay recurrent neural network classifier with two different representations of dynamic context: the time-delayed input nodes represent local dynamic context, while the recursive nodes are able to represent long-term dynamic context of voice signal. The cepstral PLP coefficients were used as speech features. In the speech recognition experiments, the MSVQ/TDRNN speech recognizer shows 97.9 % word recognition rate for speaker independent recognition.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.127-132
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• 2009

The feature vectors which are used in conventional speaker recognition (SR) systems may have many correlations between their neighbors. To improve the performance of the SR, many researchers adopted linear transformation method like principal component analysis (PCA). In general, the linear transformation of the feature vectors is based on concatenated form of the static features and their dynamic features. However, the linear transformation which based on both the static features and their dynamic features is more complex than that based on the static features alone due to the high order of the features. To overcome these problems, we propose an efficient method that applies linear transformation and temporal information of the features to reduce complexity and improve the performance in speaker verification (SV). The proposed method first performs a linear transformation by PCA coefficients. The delta parameters for temporal information are then obtained from the transformed features. The proposed method only requires 1/4 in the size of the covariance matrix compared with adding the static and their dynamic features for PCA coefficients. Also, the delta parameters are extracted from the linearly transformed features after the reduction of dimension in the static features. Compared with the PCA and conventional methods in terms of equal error rate (EER) in SV, the proposed method shows better performance while requiring less storage space and complexity.

• The Journal of the Acoustical Society of Korea
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• v.13 no.3
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• pp.63-70
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• 1994

In this Paper, we propose a new codeword tying algorithm based on a tree structured classfier. The proposed algorithm which can be viewed as a kind of soft decision using statistical properties between codewords and states has an advantage of fast construction, and guarantees a unique optimal solution. Also, it can easily be applied to any speech recognition system based on discrete hidden Markov model (HMM). Experimental results on speaker-independent isolated word recognition show error reduction of $6\%$ for the codebook of size 256 and $9\%$ for 512 size and also HMM parameter reduction of about $20\%$.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.363-371
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• 2010

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate for the speaker-independent speech recognition, we study the effect of spectral tilt on the Fourier magnitude spectrum en route to the extraction of MFCC. The effect of FIR filtering on the speech signal on the speech recognition is also investigated in parallel. Evaluation of the proposed methods are performed by two independent ways of the Fisher discriminant objective function and speech recognition test by hidden Markov model with fuzzy vector quantization. From the experiments, the recognition error rate is found to show about 10% relative improvements over the conventional method by an appropriate choice of the tilt factor.

• The Journal of the Acoustical Society of Korea
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• v.10 no.4
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• pp.5-11
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• 1991

This paper suggests a segmentation method of speech signal into phonemic units. The suggested segmentation system is speaker-independent and performed without anyprior information of speech signal. In segmentation process, we first divide input speech signal into purevoiced region and not pure voiced speech regions. After then we apply the second algorithm which segments each region into the detailed phonemic units by using the voiced detection parameters, i.e., the time variation of 0th LPC cepstrum coefficient parameter and the ZCR parameter. Types of speech, used to prove the availability of segmentation algorithm suggested in this paper, are the vocabulary composed of isolated words and continuous words. According to the experiments, the successful segmentation rate for 507 phonemic units involved in the total vocabulary is 91.7%.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.150-155
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• 2004

In this paper, an emotion recognition method using speech signal is presented. Six basic human emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. The proposed recognizer have each codebook constructed by using the wavelet transform for the emotional state. Here, we first verify the emotional state at each filterbank and then the final recognition is obtained from a multi-decision method scheme. The database consists of 360 emotional utterances from twenty person who talk a sentence three times for six emotional states. The proposed method showed more 5% improvement of the recognition rate than previous works.

• Speech Sciences
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• v.15 no.2
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• pp.177-189
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• 2008

This study was to investigate diadochokinetic (DDK) rate, regularity and mean flow rate of articulation valves in dysarthria. DDK rate, mean airflow rate (MFR) and regularity of DDK syllable repetitions of vocal function /ihi/, tongue function /ta/, velopharyngeal function /bm/, and labial function /pa/ in 24 normal and dysarthric speakers were measured. Aerophone Ⅱ and Motor Speech Profile were used for data recording and analysis. The results of the findings were as follows: First, there were significant differences between the dysarthria and the normal group in DDK rate. DDK rates in ataxic dysarthria were the lowest and spastic, flaccid, and hypokinetic dysarthria followed in sequence. Second, there was a significant difference between the dysarthria and the normal group in DDK regularity. Third, there was a significant difference between dysarthria groups and normal group in DDK MFR. Finally, there was a significant difference between the 4 groups of dysarthria and the normal group in DDK air flow tracking. The results of this study can be guidelines for normal DDK rate, regularity and flow rate in dysarthria groups. In addition, their differential diagnoses and descriptions are important to make a decision on medical and behavioral management of the individuals with disorders according to DDK characteristics.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.89-95
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• 1994

This paper describes development of a real-time voice dialing system which can recognize around one hundred word vocabularies in speaker independent mode. The voice recognition algorithm in this system is implemented on a DSP board with a telephone interface plugged in an IBM PC AT/486. In the DSP board, procedures for feature extraction, vector quantization(VQ), and end-point detection are performed simultaneously in every 10 msec frame interval to satisfy real-time constraints after detecting the word starting point. In addition, we optimize the VQ codebook size and the end-point detection procedure to reduce recognition time and memory requirement. The demonstration system has been displayed in MOBILAB of the Korean Mobile Telecom at the Taejon EXPO'93.