• Speech Sciences
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• v.13 no.3
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• pp.155-167
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• 2006

Hearing impairment children possess poor underlying perceptual knowledge of the sound system and show delayed development of segmental organization of that system. The purpose of this study was to investigate the relationship between phonological awareness ability and word identification ability in hearing impaired children. 14 children with moderately severe hearing loss participated in this study. All tasks were individually administered. Phonological awareness tests consisted of syllable blending, syllable segmentation, syllable deletion, body-coda discrimination, phoneme blending, phoneme segmentation and phoneme deletion. Close-set Monosyllabic Words(12 items) and lists 1 and 2 of open-set Monosyllabic Words in EARS-K were examined for word identification. Results of this study were as follows: First, from the phonological awareness task, the close-set word identification showed a high positive correlation with the coda discrimination, phoneme blending and phoneme deletion. The open-set word identification showed a high positive correlation with phoneme blending, phoneme deletion and phoneme segmentation. Second, from the level of phonological awareness, the close-set word identification showed a high positive correlation with the level of body-coda awareness and phoneme awareness while the open-set word identification showed a high positive correlation only with the level of phoneme awareness.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.27-36
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• 2016

Recognition of grapheme is a very important process in the recognition within 'Hangul(Korean written language)' letters using phoneme recognition. It is because the success or failure in the recognition of phoneme greatly affects the recognition of letters. For this reason, it is reported that separation of phonemes is the biggest difficulty in the phoneme recognition study. The current study separates and suggests the new phonemes that used the connective elements that are helpful for dividing phonemes, recommends the features for recognition of such suggested phonemes, databases this, and carried out a set of experiments of recognizing phonemes using the suggested features. The current study used 350 letters in the experiment of phoneme separation and recognition. In this particular kind of letters, there were 1,125 phonemes suggested. In the phoneme separation experiment, the phonemes were divided in the rate of 100%, and the phoneme recognition experiment showed the recognition rate of 98% in recognizing only 14 phonemes into different ones.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.328-335
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• 1998

한국통신의 음성인식 시스템에서 사용하는 기본 음소의 효율적인 설정을 위하여 음소인식률을 구하고 유사하게 인식되는 음소들의 집합인 cohort set을 구하여, 인식률을 최대로 하는 기본음소 집합을 찾는 방법이다. 실험 방식은 기본음소 59개로부터 시작하여 음소를1개씩 줄여가면서 최대 음소 인식률이 나오도록 하였다. 실험 결과 최고 성능을 나타내는 기본 음소 set을 구할 수 있었다.

• Journal of the Korean Society for Railway
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• v.14 no.3
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• pp.228-233
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• 2011

This paper presented the method about the Implementation of Speech Recognition based on phoneme considering the phonological characteristic for Korean Subway Station Names. The Pronunciation dictionary considering PLU set and phonological variations with four Case in order to select the optimum PLU used for Speech Recognition based on phoneme for Korean Subway Station Names was comprised and the recognition rate was estimated. In the case of the applied PLU, we could know the optimum recognition rate(97.74%) be shown in the triphone model in case of considering the recognition unit division of the initial consonant and final consonant and phonological variations.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.34-43
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• 2012

Determining the similarity between two strings can be applied various area such as information retrieval, spell checker and spam filtering. Similarity calculation between Korean strings based on dynamic programming methods firstly requires a definition of the similarity between phonemes. However, existing methods have a limitation that they use manually set similarity scores. In this paper, we propose a method to automatically calculate inter-phoneme similarity from a given set of variant words using a PAM-like probabilistic model. Our proposed method first finds the pairs of similar words from a given word set, and derives derivation rules from text alignment results among the similar word pairs. Then, similarity scores are calculated from the frequencies of variations between different phonemes. As an experimental result, we show an improvement of 10.1%~14.1% and 8.1%~11.8% in terms of sensitivity compared with the simple match-mismatch scoring scheme and the manually set inter-phoneme similarity scheme, respectively, with a specificity of 77.2%~80.4%.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.335-338
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• 2010

A new method that improves the performance of large scale voice dialling system is presented using speaker adaptation. Since SI (Speaker Independent) based speech recognition system with phoneme HMM uses only the phoneme string of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the speaker dependent system due to the mismatch between the input utterance and the SI models. A new method that estimates the phonetic string and adaptation vectors iteratively is presented to reduce the mismatch between the training utterances and a set of SI models using speaker adaptation techniques. For speaker adaptation the stochastic matching methods are used to estimate the adaptation vectors. The experiments performed over actual telephone line shows that proposed method shows better performance as compared to the conventional method. with the SI phonetic recognizer.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.4-10
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• 2000

The work presented in this paper is about a postprocessor, which improves the performance of automatic speech segmentation system by correcting the phoneme boundary errors. We propose a postprocessor that reduces the range of errors in the auto labeled results that are ready to be used directly as synthesis unit. Starting from a baseline automatic segmentation system, our proposed postprocessor trains the features of hand labeled results using multi-layer perceptron(MLP) algorithm. Then, the auto labeled result combined with MLP postprocessor determines the new phoneme boundary. The details are as following. First, we select the feature sets of speech, based on the acoustic phonetic knowledge. And then we have adopted the MLP as pattern classifier because of its excellent nonlinear discrimination capability. Moreover, it is easy for MLP to reflect fully the various types of acoustic features appearing at the phoneme boundaries within a short time. At the last procedure, an appropriate feature set analyzed about each phonetic event is applied to our proposed postprocessor to compensate the phoneme boundary error. For phonetically rich sentences data, we have achieved 19.9 % improvement for the frame accuracy, comparing with the performance of plain automatic labeling system. Also, we could reduce the absolute error rate about 28.6%.

• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.157-169
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• 2013

Using a target detection task, this study compared the processing automaticity of phonemes and features in spoken syllable stimuli to determine the primitive representation in speech perception, phoneme or distinctive feature. For this, we modified the visual search task(Treisman et al., 1992) developed to investigate the processing of visual features(ex. color, shape or their conjunction) for auditory stimuli. In our task, the distinctive features(ex. aspiration or coronal) corresponded to visual primitive features(ex. color and shape), and the phonemes(ex. /$t^h$/) to visual conjunctive features(ex. colored shapes). The automaticity is measured by the set size effect that was the increasing amount of reaction time when the number of distracters increased. Three experiments were conducted. The laryngeal features(experiment 1), the manner features(experiment 2), and the place features(experiment 3) were compared with phonemes. The results showed that the distinctive features are consistently processed faster and automatically than the phonemes. Additionally there were differences in the processing automaticity among the classes of distinctive features. The laryngeal features are the most automatic, the manner features are moderately automatic and the place features are the least automatic. These results are consistent with the previous studies(Bae et al., 2002; Bae, 2010) that showed the perceptual hierarchy of distinctive features.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.434-442
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• 2003

Software internationalization, the process of making software easier to localize for specific languages, has deep implications when applied to speech technology, where the goal of the task lies in the very essence of the particular language. A greatdeal of work and fine-tuning has gone into language processing software based on ASCII or a single language, say English, thus making a port to different languages difficult. The inherent identity of a language manifests itself in its lexicon, where its character set, phoneme set, pronunciation rules are revealed. We propose a decomposition of the lexicon building process, into four discrete and sequential steps. For preprocessing to build a lexical model, we translate from specific language code to unicode. (step 1) Transliterating code points from Unicode. (step 2) Phonetically standardizing rules. (step 3) Implementing grapheme to phoneme rules. (step 4) Implementing phonological processes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.4
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• pp.360-373
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• 1991

Since 70's, efficient speech recognition methods such as HMM or DTW have been introduced primarily for speaker dependent isolated words. These methods however have confronted with difficulties in recognizing continuous speech. Since early 80's, there has been a growing awareness that neural networks might be more appropriate for English and Japanese phoneme recognition using neural networks. Dealing with only a part of vowel or consonant set, Korean phoneme recognition still remains on the elementary level. In this light, we develop a system based on neural networks which can recognize major Korean phonemes. Through experiments using two neural networks, SOFM and TDNN, we obtained remarkable results. Especially in the case of using TDNN, the recognition rate was estimated about 93.78% for training data and 89.83% for test data.