• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.12
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• pp.1324-1335
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• 1991

In this paper, a phoneme separation and an establishment of discriminative pattern of Korean phonemes are studied on experiment. The separation uses parameters such as pitch extraction, glottal peak pulse width of each pitch. speech duration. envelope and amplitude bias. The first pitch is extracted by deviations of glottal peak and width. energy and normalization on a bias on the top of vowel envelope. And then, it traces adjacent pitch to vowel in whole. On vewel, amethod to be reduced gliding pattern and the possible of vowel distinction to be used just second formant are proposed, and shrinking pitch waveform has nothing to do with pitch length is estimated. A pattern of envelope, spectrum, shrinking waveform, and a method of analysis by mutual relation among phonemes and manners of articulation on consonant are detected. As experimental results, 90% on vowel phoneme, 80% and 60% on initial and final consonant are discriminated.

• Cross-Cultural Studies
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• v.30
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• pp.97-124
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• 2013

For a long period, vowel reduction has been accepted as one of the most common pronunciation rules in Russian phonology. However, since the rules have been modified in many ways after the influx of loanwords, [a, e, i, o, u, ${\star}$]-including [e, o]-can now be pronounced in unstressed position, obeying the rule of vowel reduction. Especially in Modern Russian, along with the destruction of the consonant pronunciation norm due to some relatively complex changes it underwent palatalization, consonant pronunciation has been simplified, and as a response to such a phenomenon, the specialization of vowel pronunciation rule is now occurring. In other words, in the interrelation between consonants and vowels, as the pronunciation rules for consonants are simplified and thus the contrast between consonants is weakened, the degree of dependence on pronunciation of segment in the vowel pronunciation rule has been elevated. Therefore, the analysis says that the degree of vowel reduction depends on a vowel's distance from a stressed syllable is not enough; the influence of surrounding phonemes-including consonants-or the formative characteristics of words themselves should also be considered. The introduction of Max-noncorner/UnderLex, a/an Licence constraint that is related to non-declension nouns, and that of IdentC[back] and ShareCV[back], which are faithfulness constraint and share constraint respectively that are related to the nature of consonants stresses that vowel pronunciation rules should not be simply viewed as rules for vowels; The rules should be analyzed with emphasis on their correlation with surrounding phonemes.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.381-388
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• 2003

This paper presents a method of giving weights to garbage class clustering and Filler model to improve performance of keyword spotting system and a time-saving method of dialogue speech processing system for keyword spotting by calculating keyword transition probability through speech analysis of task domain users. The point of the method is grouping phonemes with phonetic similarities, which is effective in sensing similar phoneme groups rather than individual phonemes, and the paper aims to suggest five groups of phonemes obtained from the analysis of speech sentences in use in Korean morphology and in stock-trading speech processing system. Besides, task-subject Filler model weights are added to the phoneme groups, and keyword transition probability included in consecutive speech sentences is calculated and applied to the system in order to save time for system processing. To evaluate performance of the suggested system, corpus of 4,970 sentences was built to be used in task domains and a test was conducted with subjects of five people in their twenties and thirties. As a result, FOM with the weights on proposed five phoneme groups accounts for 85%, which has better performance than seven phoneme groups of Yapanel [1] with 88.5% and a little bit poorer performance than LVCSR with 89.8%. Even in calculation time, FOM reaches 0.70 seconds than 0.72 of seven phoneme groups. Lastly, it is also confirmed in a time-saving test that time is saved by 0.04 to 0.07 seconds when keyword transition probability is applied.

• Journal of the Korean Institute of Telematics and Electronics
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• v.18 no.4
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• pp.27-33
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• 1981

In this paper, the effect of tone variation of speech signals is discussedty showing the variations of the linear prediction model spectra and the estimated vocal tract shape for Korean vowels. As an application of the analysis results a speech spenthesis scheme by combination of phonemes is also discussed based on experimental results.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.137-140
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• 2002

This study was conducted to examine the korean word length effects on auditory word recognition. Linguistically, word length can be defined by several sublexical units such as letters, phonemes, syllables, and so on. In order to investigate which units are used in auditory word recognition, lexical decision task was used. Experiment 1 and 2 showed that syllable length affected response time, and syllable length interacted with word frequency. As a result, in recognizing auditory word syllable length was important variable.

• Speech Sciences
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• v.9 no.3
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• pp.35-45
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• 2002

A simple algorithm for discriminating voiced sounds in a speech is proposed. In addition to low-frequency energy and zero-crossing rate (ZCR), both of which have been widely used in the past for identifying voiced sounds, the proposed algorithm incorporates pitch variation to improve the discrimination rate. Based on TIMIT corpus, evaluation result shows an improvement of 13% in the discrimination of voiced phonemes over that of the traditional algorithm using only energy and ZCR.

• The Journal of the Acoustical Society of Korea
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• v.10 no.6
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• pp.5-11
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• 1991

연속음 인식을 위하여 연결된 음향 신호를 음소단위로 분할하는 것이 필요하다. 본 논문에서는 연속 음성에서의 정상상태 구간 검출을 위한 파라미터로서 진폭분포를 이용하는 방법을 제안하였다. 제 안된 진폭분포는 음성신호의 변화특성을 정확히 나타내며 이러한 프레임사이의 진폭분포를 이용하는 방 법을 제안하였다. 제안된 지폭분포는 음성 신호의 변화특성을 정확히 나타내며 이러한 프레임사이의 진 폭 분포 차이값을 비교하여 프레임의 안정구간과 천이구간을 구분할 수 있었다.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2220-2223
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• 2003

In this paper we used Support Vector Machines(SVMs) recently proposed as the loaming method, one of Artificial Neural Network, to divide continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme divided by eye-measurement. From experiment we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.184-187
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• 1995

This paper proposes an algorithm for machine recognition of phonemes in continuous speech. The proposed algorithm is static strategy neural network. The algorithm uses, at the stage of training neuron, features such as PARCOR coefficient and auditory-like perceptual liner prediction . These features are extracted from speech samples selected by a sliding 25.6msec windows with s sliding gap being 3 msec long, then interleaved and summed up to 7 sets of parmeters covering 171 msec worth of speech for use of neural inputs. Perfomances are compared when either PARCOR or auditory-like PLP is included in the feture set.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.68-73
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• 2000

This study to embody automatic recognition of VIN(Vehicle Identification Number)character by computer vision system. Automatic recognition characters methods consist of the thining processing and the recognition of each character. VIN character and background classified using counting method of the size of connected pixels. Thining processing applied to segmentation of connected fundamental phonemes by Hilditch's algorithm. Each VIN character contours tracing algorithm used the Freeman's direction tracing algorithm.