• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.20-23
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• 1999

In this paper, The Split Model Analysis Algorithm, which can generate the wideband speech signal from the spectral information of narrowband signal, is developed. The Split Model Analysis Algorithm deals with the separation of the $10^{th}$ order LPC model into five cascade-connected $2^{nd}$ order model. The use of the less complex $2^{nd}$ order models allows for the exclusion of the complicated nonlinear relationships between model parameters and all the poles of the LPC model. The relationships between the model parameters and its corresponding analog poles is proved and applied to each $2^{nd}$ order model. The wideband speech signal is obtained by changing only the sampling rate

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1135-1138
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• 1999

In this paper, The Split Model Analysis Algorithm, which can generate the wideband speech signal from the spectral information of narrowband signal, is developed. The Split Model Analysis Algorithm deals with the separation of the 10$\^$th/ order LPC model into five cascade-connected 2$\^$nd/ order model. The use of the less complex 2$\^$nd/ order models allows for the exclusion of the complicated nonlinear relationships between model parameters and all the poles of the LPC model. The relationships between the model parameters and its corresponding analog poles is proved and applied to each 2$\^$nd/ order model. The wideband speech signal is obtained by changing only the sampling rate.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.269-274
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• 2004

Noisy speech recognition is one of most important problems in speech recognition. In this paper, a method which efficiently removes the mixed noise with speech, is proposed. The proposed method is based on the ICA to separate the mixed noise. ICA(Independent component analysis) is a signal processing technique, whose goal is to express a set of random variables as linear combinations of components that are statistically as independent from each other as possible.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.67-72
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• 1991

This paper evaluates thequality of synthesized speech by rule using the LPC CD in the objective measure and then compares the result with the subjective analysis. By evaluating the quality of synthesized speech by rule objectively. We have tried to resolve the problems (Evaluation time or size expansion, variables within the analysis results) that arise when the evaluation is done subjectively. Also by comparing intelligibility-the index for the subjective quality evaluation of synthesized speech by rule-with evaluation results obtained using MOS and the objective evaluation. We have proved the validity of the objective analysis and thus provides a guide that would be useful when R&D and marketing of synthesis by rule method is done.

• Phonetics and Speech Sciences
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• v.9 no.2
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• pp.85-94
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• 2017

Conventional speaker verification systems using time delay neural network, identity vector and probabilistic linear discriminant analysis (TDNN-Ivector-PLDA) are known to be very effective for verifying long-duration speech utterances. However, when test utterances are of short duration, duration mismatch between enrollment and test utterances significantly degrades the performance of TDNN-Ivector-PLDA systems. To compensate for the I-vector mismatch between long and short utterances, this paper proposes to use probabilistic linear discriminant analysis (PLDA) model adaptation with augmented data. A PLDA model is trained on vast amount of speech data, most of which have long duration. Then, the PLDA model is adapted with the I-vectors obtained from short-utterance data which are augmented by using vocal tract length perturbation (VTLP). In computer experiments using the NIST SRE 2008 database, the proposed method is shown to achieve significantly better performance than the conventional TDNN-Ivector-PLDA systems when there exists duration mismatch between enrollment and test utterances.

• Phonetics and Speech Sciences
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• v.10 no.1
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• pp.49-55
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• 2018

This paper aims to describe a knowledge-based pronunciation generation system for French. It has been reported that a rule-based pronunciation generation system outperforms most of the data-driven ones for French; however, only a few related studies are available due to existing language barriers. We provide basic information about the French language from the point of view of the relationship between orthography and pronunciation, and then describe our knowledge-based pronunciation generation system, which consists of morphological analysis, Part-of-Speech (POS) tagging, grapheme-to-phoneme generation, and phone-to-phone generation. The evaluation results show that the word error rate of POS tagging, based on a sample of 1,000 sentences, is 10.70% and that of phoneme generation, using 130,883 entries, is 2.70%. This study is expected to contribute to the development and evaluation of speech synthesis or speech recognition systems for French.

• Speech Sciences
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• v.11 no.1
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• pp.43-52
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• 2004

This paper concerns an effective dual-channel noise reduction method to increase the performance of speech recognition in a car environment. While various single channel methods have already been developed and dual-channel methods have been studied somewhat, their effectiveness in real environments, such as in cars, has not yet been formally proven in terms of achieving acceptable performance level. Our aim is to remedy the low performance of the single and dual-channel noise reduction methods. This paper proposes an effective dual-channel noise reduction method based on a high-pass filter and front-end processing of the eigendecomposition method. We experimented with a real multi-channel car database and compared the results with respect to the microphones arrangements. From the analysis and results, we show that the enhanced eigendecomposition method combined with high-pass filter indeed significantly improve the speech recognition performance under a dual-channel environment.

• Speech Sciences
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• v.9 no.1
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• pp.27-38
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• 2002

The purpose of the study is to examine intonation patterns of cochlear implanted children compared with those of normal hearing children. The data tokens of three normal and five cochlear implanted children were collected and investigated. Their intonation patterns were analyzed using the speech analysis tool, Praat. The characteristics of the two utterance types, interrogative and declarative, were investigated. No significant difference in intonation patterns between the two subject groups was found. However, the general pitch of cochlear implanted children was higher than that of normal hearing children. In addition, cochlear implanted children showed frequent pitch breaks.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2238-2245
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• 2000

In this papr, eatue parameters, extracted using Wavelet transform for Korean isolated worked speech, are sued for speech detection and recognition feature. As a result of the speech detection, it is shown that it produces more exact detection result than eh method of using energy and zero-crossing rate on speech boundary. Also, as a result of the method with which the feature parameter of MFCC, which is applied to he recognition, it is shown that the result is equal to the result of the feature parameter of MFCC using FFT in speech recognition. So, it has been verified the usefulness of feature parameters using Wavelet transform for speech analysis and recognition.

• MALSORI
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• no.56
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• pp.1-13
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• 2005

This study examined the speech intelligibility of one-syllable words with phonetic contrasts and analyzed segmental factors that can predict the overall speech intelligibility in hearing-impaired adults. To identify the speech error characteristics, a Korean word list was audio-recorded by 7 hearing-impaired adults, and 35 listeners selected the heard word out of 5 choices. Based in part on previous studies of speech of the hearing impaired, the word list consisted of monosyllabic consonant-vowel-consonant (CVC) real word pairs. Stimulus words included 77 phonetic contrast pairs. The results showed that the percentage of errors in final position (coda) contrast was higher than in any other position in syllable. And the intelligibility deficit factors of phonetic contrast in the hearing-impaired were analyzed through stepwise regression analysis. The overall intelligibility was predicted by the error rate of manner contrast at coda, voicing contrast (homorganic triplets) at onset and high-low contrast at nucleus.