• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.456-463
• /
• 1996

In this paper, we discussed three cases to see the effects of the characteristics of Hangul writing system. In applications such as computer Hangul shorthands for ordinary people and pushbuttons with Hangul characters engraved, we found that there is much advantage in using Hangul. In case of Hangul Transliteration, we discussed some problems which are related with the characteristics of Hangul writing system. Shorthands use 3-set keyboards in England, America, and Korea. We saw how ordinary people can do computer Hangul shorthands, whereas only experts can do computer shorthands in other countries. Specifically, the facts that 1) Hangul characters are grouped into syllables (syllabic blocks) and that 2) there is already a 3-set Hangul keyboard for ordinary people allow ordinary people to do computer Hangul shorthands without taking special training as with English shorthands. This study was done by the author under the codename of 'Sejong 89'. In contrast like QWERTY or DVORAK, a 2-set Hangul keyboard cannot be used for shorthands. In case of English pushbuttons, one digit is associated with only one character. However, by engraving only syllable-initial characters on the phone pushbuttons, we can associate one Hangul "syllable" with one digit. Therefore, for a given number of digits, we can associate longer words or more meaningful words in Hangul than in English. We discussed the problems of the Hangul Transliteration system proposed by South Korea and suggested their solutions, if available. 1) We are incorrectly using the framework of transcription for transliteration. To solve the problem, the author suggests that a) we include all complex characters in the transliteration table, and that b) we specify syllable-initial and -final characters separately in the table. 2) The proposed system cannot represent independent characters and incomplete syllables. 3) The proposed system cannot distinguish between syllable-initial and -final characters.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.1
• /
• pp.68-79
• /
• 2011

This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposing technique proved that the actual performance is excellent by comparison experiment with other techniques. Performance evaluation of function extracting positive/negative opinion information, the performance evaluation applying dynamic window technique and tokenizer technique for multilingual information retrieval, and the performance evaluation of technique extracting exact multilingual phonetic translation are carried out. The experiment with typical movie review sentence and Wikipedia experiment data as object as that applying example is carried out and the result is analyzed.

• MALSORI
• /
• no.43
• /
• pp.1-9
• /
• 2002

This study aims to model Korean prosodic phrasing using CART(classification and regression tree) method. Our data are limited to Korean read speech. We used 400 sentences made up of editorials, essays, novels and news scripts. Professional radio actress read 400sentences for about two hours. We used K-ToBI transcription system. For technical reason, original break indices 1,2 are merged into AP. Differ from original K-ToBI, we have three break index Zero, AP and IP. Linguistic information selected for this study is as follows: the number of syllables in ‘Eojeol’, the location of ‘Eojeol’ in sentence and part-of-speech(POS) of adjacent ‘Eojeol’s. We trained CART tree using above information as variables. Average accuracy of predicting NonIP(Zero and AP) and IP was 90.4% in training data and 88.5% in test data. Average prediction accuracy of Zero and AP was 79.7% in training data and 78.7% in test data.

• Phonetics and Speech Sciences
• /
• v.8 no.4
• /
• pp.103-113
• /
• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• MALSORI
• /
• no.65
• /
• pp.93-103
• /
• 2008

In this paper, we propose a method for optimizing a multiple pronunciation dictionary used for modeling pronunciation variations of non-native speech. The proposed method removes some confusable pronunciation variants in the dictionary, resulting in a reduced dictionary size and less decoding time for automatic speech recognition (ASR). To this end, a confusability measure is first defined based on the Levenshtein distance between two different pronunciation variants. Then, the number of phonemes for each pronunciation variant is incorporated into the confusability measure to compensate for ASR errors due to words of a shorter length. We investigate the effect of the proposed method on ASR performance, where Korean is selected as the target language and Korean utterances spoken by Chinese native speakers are considered as non-native speech. It is shown from the experiments that an ASR system using the multiple pronunciation dictionary optimized by the proposed method can provide a relative average word error rate reduction of 6.25%, with 11.67% less ASR decoding time, as compared with that using a multiple pronunciation dictionary without the optimization.

• MALSORI
• /
• no.57
• /
• pp.123-137
• /
• 2006

This paper aims to propose a method of detecting irregular pronunciations for Korean POI data adopting the notion of the Prosodic Word based on the Prosodic Phonology (Selkirk 1984, Nespor and Vogel 1986) and Intonational Phonology (Jun 1996). In order to show the performance of the proposed method, the detection experiment was conducted on the 250,000 POI data. When all the data were trained, 99.99% of the exceptional prosodic words were detected, which shows the stability of the system. The results show that similar ratio of exceptional prosodic words (22.4% on average) were detected on each stage where a certain amount of the training data were added. Being intended to be an example of an interdisciplinary study of linguistics and computer science, this study will, on the one hand, provide an understanding of Korean language from the phonological point of view, and, on the other hand, enable a systematic development of a multiple pronunciation lexicon for Korean TTS or ASR systems of high performance.

• MALSORI
• /
• no.44
• /
• pp.73-82
• /
• 2002

Channel distortion degrades the performance of speech recognizer in telephone environment. It mainly results from the bandwidth limitation and variation of transmission channel. Variation of channel characteristics is usually represented as baseline shift in the cepstrum domain. Thus undesirable effect of the channel variation can be removed by subtracting the mean from the cepstrum. In this paper, to improve the recognition performance of Korea connected digit telephone speech, channel compensation methods such as CMN (Cepstral Mean Normalization), RTCN (Real Time Cepatral Normalization), MCMN (Modified CMN) and MRTCN (Modified RTCN) are applied to the static MFCC. Both MCMN and MRTCN are obtained from the CMN and RTCN, respectively, using variance normalization in the cepstrum domain. Using HTK v3.1 system, recognition experiments are performed for Korean connected digit telephone speech database released by SITEC (Speech Information Technology & Industry Promotion Center). Experiments have shown that MRTCN gives the best result with recognition rate of 90.11% for connected digit. This corresponds to the performance improvement over MFCC alone by 1.72%, i.e, error reduction rate of 14.82%.

• Phonetics and Speech Sciences
• /
• v.7 no.1
• /
• pp.139-150
• /
• 2015

The data accumulated in this database will be used to develop a speaker identification system. This may also be applied towards, but not limited to, fields of phonetic studies, sociolinguistics, and language pathology. We plan to supplement the large-scale speech corpus next year, in terms of research methodology and content, to better answer the needs of diverse fields. The purpose of this study is to develop a speech corpus for standard Korean speech. For the samples to viably represent the state of spoken Korean, demographic factors were considered to modulate a balanced spread of age, gender, and dialects. Nine separate regional dialects were categorized, and five age groups were established from individuals in their 20s to 60s. A speech-sample collection protocol was developed for the purpose of this study where each speaker performs five tasks: two reading tasks, two semi-spontaneous speech tasks, and one spontaneous speech task. This particular configuration of sample data collection accommodates gathering of rich and well-balanced speech-samples across various speech types, and is expected to improve the utility of the speech corpus developed in this study. Samples from 639 individuals were collected using the protocol. Speech samples were collected also from other sources, for a combined total of samples from 1,012 individuals.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.397-400
• /
• 2003

본 연구에서는 KM-Net(Hidden Markov Network)을 다양한 태스크에의 적용과 화자의 특성을 효과적으로 나타내기 위해 HM-Net 음성인식 시스템에 MLLR(Maximum Likelihood Linear Regression) 적응방법을 도입하였으며, HM-Net 학습 알고리즘을 개량하여 회귀클래스 생성방법을 제안한다. 제안방법은 PDT-SSS(Phonetic Decision Tree-based Successive State Splitting) 알고리즘의 문맥방향 상태분할에 의한 상태레벨 공유를 이용한 방법으로 새로운 화자로부터 문맥정보와 적응화 데이터의 발성 양에 의존하여 결정된 많은 적응 파라미터들을(평균, 분산) 자유롭게 제어할 수 있게 된다. 제안방법의 유효성을 확인하기 위해 국어공학센터(KLE) 452 음성 데이터와 항공편 예약관련 연속음성을 대상으로 인식실험을 수행한 결과, 전체적으로 음소인식의 경우 평균 34-37％, 단어인식의 경우 평균 9％, 연속음성인식의 경우 평균 7-8％의 인식성능 향상을 각각 보였다. 또한 적응화 데이터의 양에 따른 인식성능 비교에서, 제안방법을 적용한 인식 시스템이 적응 데이터의 양이 적은 경우에도 향상된 인식률을 보였으며. 잡음을 부가한 음성에 대한 적응화 실험에서도 향상된 인식성능을 보여 MLLR 적응방법의 특성을 만족하였다. 따라서 MLLR 적응방법을 도입한 HM-Net 음성인식 시스템에 제안한 회귀클래스 생성방법이 유효함을 확인한 수 있었다.

• MALSORI
• /
• no.59
• /
• pp.27-51
• /
• 2006

For speech recognition systems, rejection function as well as decoding function is necessary to improve the reliability. There have been many research efforts on out-of-vocabulary word rejection, however, little attention has been paid on non-target sentence rejection. Recently pronunciation approaches using speech recognition increase the need for non-target sentence rejection to provide more accurate and robust results. In this paper, we proposed filler model method and word/phoneme detection ratio method to implement non-target sentence rejection system. We made performance evaluation of filler model along to word-level, phoneme-level, and sentence-level filler models respectively. We also perform the similar experiment using word-level and phoneme-level word/phoneme detection ratio method. For the performance evaluation, the minimized average of FAR and FRR is used for comparing the effectiveness of each method along with the number of words of given sentences. From the experimental results, we got to know that word-level method outperforms the other methods, and word-level filler mode shows slightly better results than that of word detection ratio method.