• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.160-166
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• 2002

In this paper, we implement a new vocabulary-independent speech recognition system that uses CV, VCCV, VC recognition unit. Since these recognition units are extracted in the trowel region of syllable, the segmentation is easy and robust. And in the case of not existing VCCV unit, the units are replaced by combining VC and CV semi-syllable model. Clustering of vowel group and applying combination rule to the substitution model in the case of not existing of VCCV model lead to 5.2% recognition performance improvement from 90.4% (Model A) to 95.6% (Model C) in the first candidate. The recognition results that is 98.8% recognition rate in the second candidate confirm the effectiveness of the proposed method.

• Phonetics and Speech Sciences
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• v.14 no.2
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• pp.11-17
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• 2022

English vowels play an important role in verbal communication. However, Korean students tend to experience difficulty pronouncing a certain set of vowels despite extensive education in English. The aim of this study is to apply speech recognition software to evaluate Korean students' pronunciation of English vowels in minimal pair words and then to examine acoustic characteristics of the pairs in order to check their pronunciation problems. Thirty female Korean college students participated in the recording. Speech recognition rates were obtained to examine which English vowels were correctly pronounced. To compare and verify the recognition results, such acoustic analyses as the first and second formant trajectories and durations were also collected using Praat. The results showed an overall recognition rate of 54.7%. Some students incorrectly switched the tense and lax counterparts and produced the same vowel sounds for qualitatively different English vowels. From the acoustic analyses of the vowel formant trajectories, some of these vowel pairs were almost overlapped or exhibited slight acoustic differences at the majority of the measurement points. On the other hand, statistical analyses on the first formant trajectories of the three vowel pairs revealed significant differences throughout the measurement points, a finding that requires further investigation. Durational comparisons revealed a consistent pattern among the vowel pairs. The author concludes that speech recognition and analysis software can be useful to diagnose pronunciation problems of English-language learners.

• Journal of Audiology & Otology
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• v.25 no.3
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• pp.124-130
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• 2021

Background and Objectives: The study investigated vowel context effects on the perception of stop consonants in Malayalam. It also probed into the role of vowel context effects in determining the frequency of occurrence of various consonant-vowel (CV) syllables in Malayalam. Subjects and Methods: The study used a cross-sectional pre-experimental post-test only research design on 30 individuals with normal hearing, who were native speakers of Malayalam. The stimuli included three stop consonants, each spoken in three different vowel contexts. The resultant nine syllables were presented in original form and five gating conditions. The consonant recognition in different vowel contexts of the participants was assessed. The frequency of occurrence of the nine target syllables in the spoken corpus of Malayalam was also systematically derived. Results: The consonant recognition score was better in the /u/ vowel context compared with /i/ and /a/ contexts. The frequency of occurrence of the target syllables derived from the spoken corpus of Malayalam showed that the three stop consonants occurred more frequently with the vowel /a/ compared with /u/ and /i/. Conclusions: The findings show a definite vowel context effect on the perception of the Malayalam stop consonants. This context effect observed is different from that in other languages. Stop consonants are perceived better in the context of /u/ compared with the /a/ and /i/ contexts. Furthermore, the vowel context effects do not appear to determine the frequency of occurrence of different CV syllables in Malayalam.

• Korean Journal of Audiology
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• v.25 no.3
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• pp.124-130
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• 2021

Background and Objectives: The study investigated vowel context effects on the perception of stop consonants in Malayalam. It also probed into the role of vowel context effects in determining the frequency of occurrence of various consonant-vowel (CV) syllables in Malayalam. Subjects and Methods: The study used a cross-sectional pre-experimental post-test only research design on 30 individuals with normal hearing, who were native speakers of Malayalam. The stimuli included three stop consonants, each spoken in three different vowel contexts. The resultant nine syllables were presented in original form and five gating conditions. The consonant recognition in different vowel contexts of the participants was assessed. The frequency of occurrence of the nine target syllables in the spoken corpus of Malayalam was also systematically derived. Results: The consonant recognition score was better in the /u/ vowel context compared with /i/ and /a/ contexts. The frequency of occurrence of the target syllables derived from the spoken corpus of Malayalam showed that the three stop consonants occurred more frequently with the vowel /a/ compared with /u/ and /i/. Conclusions: The findings show a definite vowel context effect on the perception of the Malayalam stop consonants. This context effect observed is different from that in other languages. Stop consonants are perceived better in the context of /u/ compared with the /a/ and /i/ contexts. Furthermore, the vowel context effects do not appear to determine the frequency of occurrence of different CV syllables in Malayalam.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.364-367
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• 1994

In this paper, we carried out some experiments on the Korean vowel recognition using the fractal dimension of the speech signals. We chose the Mincowski-Bouligand dimensioni as the fractal dimension, and computed it using the morphological covering method. For our experiments, we used both the fractal dimension and the LPC cepstrum which is conventionally known to be one of the best parameters for speech recognition, and examined the usefulness of the fractal dimension. From the vowel recognition experiments under various consonant contexts, we achieved the vowel recognition error rats of 5.6% and 3.2% for the case with only LPC cepstrum and that with both LPC cepstrum and the fractal dimension, respectively. The results indicate that the incorporation of the fractal dimension with LPC cepstrum gies more than 40% reduction in recognition errors, and indicates that the fractal dimension is a useful feature parameter for speech recognition.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.721-727
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• 2003

This study designed and implemented a system to extract of vowel from a word. The system is comprised of a voice feature extraction module and a neutral network module. The voice feature extraction module use a LPC(Linear Prediction Coefficient) model to extract a voice feature from a word. The neutral network module is comprised of a learning module and voice recognition module. The learning module sets up a learning pattern and builds up a neutral network to learn. Using the information of a learned neutral network, a voice recognition module extracts a vowel from a word. A neutral network was made to learn selected vowels(a, eo, o, e, i) to test the performance of a implemented vowel extraction recognition machine. Through this experiment, could confirm that speech recognition module extract of vowel from 4 words.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.1-10
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• 1988

In this study, the recognition experiments for Korean vowel are performed using peripheral auditory model. In addition, for the purpose of objective comparison, the recognition experiments are performed by extracting LPC cepstrum coefficients for the same speech data. The results are as follows. 1) The time and the frequency responses of the auditory model show that important features of input signal are involved in the responses of inner ear and auditory nerve. 2) The recognition results for Korean vowel show that the recognition rate by auditory model output is higher than the recognition rate by LPC cepstrum coefficients. 3) The adaptation phenomenon of auditory nerve provides useful characteristics for the discrimination of vowel signal.

• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.1-6
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• 2003

In this paper, we present a new Korean connected digit recognition based on vowel string and number of syllables. There are two steps to reduce digit candidates. The first one is to determine the number and interval of digit. Once the number and interval of digit are determined, the second is to recognize the vowel string in the digit string. The digit candidates according to vowel string are recognized based on CV (consonant vowel), VCCV and VC unit HMM. The proposed method can cope effectively with the coarticulation effects and recognize the connected digit speech very well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1140-1148
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• 1994

In this paper, we carried out some experiments on the Korean vowel recognition using the fractal dimension of the speech signals. We chose the Minkowski-Bouligand dimension as the fractal dimension, and computed it using the morphological covering method. For our experiments, we used both the fractal dimension and the LPC cepstrum which is conventionally known to be one of the best parameters for speech recognition, and examined the usefulness of the fractal dimension. From the vowel recognition experiments under various consonant contexts, we achieved the vowel recognition error rates of 5.6% and 3.2% for the case with only LPC cepstrum and that with both LPC cepstrum and the fractal dimension, respectively. The results indicate that the incorporation of the fractal dimension with LPC cepstrum gives more than 40% reduction in recognition errors, and indicates that the fractal dimension is a useful feature parameter for speech recognition.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.27-37
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• 1991

A recognition algorithm for Hangul is developed by structural analysis to Hangul in this theses. Four major procedures are proposed : preprocessing, type classification, separation of consonant and vowel, recognition. In the preprocessing procedure, the thinning algorithm proposed by CHEN & HSU is applied. In the type classification procedure, thinned Hangul image is classified into one of six formal types. In the separation of consonant and vowel procedure, starting from branch-points which are existed in a vowel, character elements are separated by means of tracing branch-point pixel by pixel and comparison with proposed templates. In the same time, the vowels are recognized. In the recognition procedure, consonants are extracted from the separated Hangul character and recognized by modified Crossing method. Recognized characters are converted into KS-5601-1989 codes. The experiments show that correct recognition rate is about 80%-90% and recognition speed is about 2-3 character persecond in three types of different input data on computer with 80386 microprocessor.