Among the types of dysarthria, spastic and flaccid types are the most prominent manifestations. The objectives of the present research are (1) to discover the phonetic contrasts that differentiate spastic dysarthria from flaccid dysarthria, (2) to analyze the degrees of predictability of each phonetic contrast for intelligibility in spastic and flaccid dysarthrias and to compare them. The 'phonemic contrast word intelligibility pairs' for dysarthric speakers were tested and proved to be useful for clinical assessment of and research on dysarthria. In the group of spastic type, it showed that initial fricative vs. affricate and front vs. back vowel contrasts are transmitted relatively less effectively than flaccid type. In the group of flaccid type, initial glottal vs null contrast is transmitted less effectively than spastic type. The overall intelligibility of spastic dysarthria was predicted by multiple regression analysis with 88% accuracy by three phonetic contrasts(initial fricative vs. affricate; front vs. back vowels; initial consonant correlates). And the intelligibility of flaccid dysarthria was predicted by two phonetic contrasts(initial nasal vs. stop, front vs. back vowels) with 60% accuracy.

Sasang Constitutional Medicine, a branch of traditional Korean medicine, believes that the health of human beings can be promoted by taking advantage of the fact that people have different constitutions. It utilizes the characteristics in human voice to diagnose the constitution of the patients. This study aims at establishing the relationship between Sasang constitutions and their corresponding voice characteristics by investigating source-related variables. Voice recordings of 23 patients from three different constitutions were obtained whose constitutions had been already diagnosed by the experts in the fields. Fundamental frequency related variables (average pitch, maximum/minimum pitch, pitch range), phonation type, speaking tempo were measured and analyzed for each group. The phonation type seemed to be a possible candidate for a successful variable to determine constitution. No statistically significant relationship was manifested between other variables and constitutions. Despite its failure to firmly establish the relationship between voice and constitutions, the current study suggests that future research should include not only source-related variables

The aim of this study is to clarify the process of the sound changes of Japanese consonants (Dakuons) in the analysis of the transcriptions of Korean Source-Materials (i.e. Japanese textbooks for Korean and the records of travel in Japan) written in the 15-l8th centuries with the Korean writing system. Especially these records of travel in Japan are meaningful in that the process of change of Dakuon is shown in detail. The results are as follows. 1) In the 15th century, all Dakuons /g d z b/ had nasality. 2) The nasality of /z/ and /b/ disappeared in the 16th century. 3) The nasality of /d/ disappeared in the late 17th century.

This paper presents a systematic description of exceptional pronunciations for automatic Korean pronunciation generation. An automatic pronunciation generator in Korean is an essential part of a Korean speech recognition system and a TTS (Text-To-Speech) system. It is composed of a set of regular rules and an exceptional pronunciation dictionary. The exceptional pronunciation dictionary is created by extracting the words that have exceptional pronunciations, based on the characteristics of the words of exceptional pronunciation through phonological research and the systematic analysis of the entries of Korean dictionaries. Thus, the method contributes to improve performance of automatic pronunciation generator in Korean as well as the performance of speech recognition system and TTS system in Korean.

음성 영어교육에서 각 음소(모음.자음) 음가 인식이 중요한 것은 물론이지만, 실제로 영어를 듣고 말하는 데는 강세모음과 약음(주로 음)이 번갈아들며 엮어내는 영어 특유 말씨 박자에 대한 이해가 오히려 더 중요한 면이 있다. 그런데 영어 철자가 자음 정보는 비교적 잘 보여주지만, 모음 정보는 정확히 알려주지 못한다. 이를테면 (equation omitted)를 적는 철자가 각각 20가지 이상이며, 특히 쭉정모음(schwa) $\partial$를 적는 철자는 무려 90가지가 넘는다. 이 $\partial$는 강세 그늘에서 비록 짧고 약하게 발음되지만 영어에서 딴 어느 모음보다도 훨씬 높은 빈도를 보이기 때문에 [(equation omitted)] 음가와 그것이 있는 자리, 즉 철자 밑에 숨어 그 존재를 알기 어려운 $\partial$ 음의 소재를 철저히 파악하는 것은 영어를 잘 듣고 영어답게 말하는 데에 필수 요체라 할 수 있다. 이 글에서는 쭉정모음(schwa) [$\partial$]를 포함한 영어 주요 모음 철자 다양성을 고찰하여 우리나라 음성 영어 교육(TEFL/TESOL)에 귀중한 참고 자료를 제공하고, 이를 통한 올바른 영어 발음 학습 태도를 제언한다.

In this paper, we propose a speech recognition method based on independent component analysis (ICA) and spectral enhancement techniques. While ICA tris to separate speech signal from noisy speech using multiple channels, some noise remains by its algorithmic limitations. Spectral enhancement techniques can compensate for lack of ICA's signal separation ability. From the speech recognition experiments with instantaneous and convolved mixing environments, we show that the proposed approach gives much improved recognition accuracies than conventional methods.

Since telephone channel has bandlimited frequency characteristics, speech signal over the telephone channel shows degraded speech quality. In this paper, we propose an algorithm using neural network to reconstruct wideband speech from its narrowband version. Although single neural network is a good tool for direct mapping, it has difficulty in training for vast and complicated data. To alleviate this problem, we modularize the neural networks based on appropriate clustering of the acoustic space. We also introduce fuzzy computing to compensate for probable misclassification at the cluster boundaries. According to our simulation, the proposed algorithm showed improved performance over the single neural network and conventional codebook mapping method in both objective and subjective evaluations.

This paper deals with a new parameter for voice detection which is used for many areas of speech engineering such as speech synthesis, speech recognition and speech coding. CV (Coefficient of Variation) of speech spectrum as well as other feature parameters is used for the detection of speech. CV is calculated only in the specific range of speech spectrum. Average magnitude and spectral magnitude are also employed to improve the performance of detector. From the experimental results the proposed voice detector outperformed the conventional energy-based detector in the sense of error measurements.

source of speech signal is actually composed of combination of periodic and aperiodic components, although it is often modeled to either one of those. In the paper an experiment which can separate periodic and aperiodic components from speech source. Linear predictive residual signal was used as a approximated vocal source the original speech to obtain the estimated aperiodic part. Iterative extrapolation method was used to compute the aperiodic part.

An automatic pronunciation correction system provides learners with correction guidelines for each mispronunciation. In this paper we propose an HMM based speech recognizer which automatically classifies pronunciation errors when Korean speak Japanese. For this purpose we also develop phoneme recognizers for Korean and Japanese. Experimental results show that the machine scores of the proposed recognizer correlate with expert ratings well.

Researches into robust recognition in noise environments, especially in car environments, are being carried out actively in speech community. In this paper we will report on three types of corpora that SiTEC (Speech Information TEchnology & industry promotion Center) has created for research into speech recognition in car noise environments. The first is the recordings of 900 Korean native speakers, distributed according to gender, age, and region, who uttered application words in car environments. The second is the collections of mixed noise in 3 car types by model while setting up various noise patterns which can be obtained with the car engine on or off, at different driving speed, and in different road conditions with windows open or closed. The third is the recordings of simulated speech by HATS (Head and Torso Simulator) in car environments with the internal and external noise factors added. These three types of recordings were all made through synchronized 8 channel microphones that are fixed in a car. The creation and applications of these corpora will be reported on in detail.

This paper proposes a noise reduction-based speech detection method under telephone channel environments. We adopt the AURORA front-end noise reduction algorithm based on the two-stage mel-warped Wiener filter approach as a preprocessor for the frequency domain speech detector. The speech detector utilizes mel filter-bank based useful band energies as its feature parameters. The preprocessor firstly removes the adverse noise components on the incoming noisy speech signals and the speech detector at the next stage detects proper speech regions for the noise-reduced speech signals. Experimental results show that the proposed noise reduction-based speech detection method is very effective in improving not only the performance of the speech detector but also that of the subsequent speech recognizer.