• 한국음향학회지
• /
• 제22권5호
• /
• pp.388-402
• /
• 2003

In this paper, we carried out the word, 4 continuous digits. continuous, and task-independent word recognition experiments to verify the effectiveness of the re-defined phoneme-likely units (PLUs) for the phonetic decision tree based HM-Net (Hidden Markov Network) context-dependent (CD) acoustic modeling in Korean appropriately. In case of the 48 PLUs, the phonemes /ㅂ/, /ㄷ/, /ㄱ/ are separated by initial sound, medial vowel, final consonant, and the consonants /ㄹ/, /ㅈ/, /ㅎ/ are also separated by initial sound, final consonant according to the position of syllable, word, and sentence, respectively. In this paper. therefore, we re-define the 39 PLUs by unifying the one phoneme in the separated initial sound, medial vowel, and final consonant of the 48 PLUs to construct the CD acoustic models effectively. Through the experimental results using the re-defined 39 PLUs, in word recognition experiments with the context-independent (CI) acoustic models, the 48 PLUs has an average of 7.06%, higher recognition accuracy than the 39 PLUs used. But in the speaker-independent word recognition experiments with the CD acoustic models, the 39 PLUs has an average of 0.61% better recognition accuracy than the 48 PLUs used. In the 4 continuous digits recognition experiments with the liaison phenomena. the 39 PLUs has also an average of 6.55% higher recognition accuracy. And then, in continuous speech recognition experiments, the 39 PLUs has an average of 15.08% better recognition accuracy than the 48 PLUs used too. Finally, though the 48, 39 PLUs have the lower recognition accuracy, the 39 PLUs has an average of 1.17% higher recognition characteristic than the 48 PLUs used in the task-independent word recognition experiments according to the unknown contextual factor. Through the above experiments, we verified the effectiveness of the re-defined 39 PLUs compared to the 48PLUs to construct the CD acoustic models in this paper.

• 말소리와 음성과학
• /
• 제7권3호
• /
• pp.139-149
• /
• 2015

This paper aims to analyze pronunciation variations of loanwords produced by Korean and improve the performance of pronunciation modeling of loanwords in Korean by using syllable-based segmentation and phonological knowledge. The loanword text corpus used for our experiment consists of 14.5k words extracted from the frequently used words in set-top box, music, and point-of-interest (POI) domains. At first, pronunciations of loanwords in Korean are obtained by manual transcriptions, which are used as target pronunciations. The target pronunciations are compared with the standard pronunciation using confusion matrices for analysis of pronunciation variation patterns of loanwords. Based on the confusion matrices, three salient pronunciation variations of loanwords are identified such as tensification of fricative [s] and derounding of rounded vowel [ɥi] and [$w{\varepsilon}$]. In addition, a syllable-based segmentation method considering phonological knowledge is proposed for loanword pronunciation modeling. Performance of the baseline and the proposed method is measured using phone error rate (PER)/word error rate (WER) and F-score at various context spans. Experimental results show that the proposed method outperforms the baseline. We also observe that performance degrades when training and test sets come from different domains, which implies that loanword pronunciations are influenced by data domains. It is noteworthy that pronunciation modeling for loanwords is enhanced by reflecting phonological knowledge. The loanword pronunciation modeling in Korean proposed in this paper can be used for automatic speech recognition of application interface such as navigation systems and set-top boxes and for computer-assisted pronunciation training for Korean learners of English.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2003년도 5월 학술대회지
• /
• pp.71-74
• /
• 2003

In this paper, we present some of experimental results developed in computer-based English Pronunciation Correction System for Korean speakers. The aim of the system is to detect incorrectly pronounced phonemes in spoken words and to give correction comment to users. Speech data were collected from 254 native speakers and 411 Koreans, then used for phoneme modeling and test. We built two types of acoustic phoneme models: native speaker model and Korean speaker model. We also built langugage models to reflect Koreans' commonly occurred mispronunications. The detection rate was over 90% in insertion/deletion/replacement of phonemes, but we got under 75% detection rate in diphthong split and accents.

• 대한음성학회지:말소리
• /
• 제43호
• /
• pp.137-150
• /
• 2002

Speech segmentation at phoneme level is important for corpus-based text-to-speech synthesis. In this paper, we examine acoustic modeling methods to improve the performance of automatic speech segmentation system based on Hidden Markov Model (HMM). We compare monophone and triphone models, and evaluate several model training approaches. In addition, we employ an energy-based postprocessing scheme to make correction of frequent boundary location errors between silence and speech sounds. Experimental results show that our system provides 71.3% and 84.2% correct boundary locations given tolerance of 10 ms and 20 ms, respectively.

• The Journal of the Acoustical Society of Korea
• /
• 제15권3E호
• /
• pp.60-65
• /
• 1996

This paper describes on the Korean continuous speech recognition system for flight reservation. It adopts a frame-synchronous One-Pass DP search algorithm driven by syntactic constraints of context free grammar(CFG). For recognition, 48 phoneme-like units(PLU) were defined and used as basic units for acoustic modeling of Korean. This modeling was conducted using a HMM technique, where each model has 4-states 3-continuous output probability distributions and 3-discrete-duration distributions. Language modeling by CFG was also applied to the task domain of flight reservation, which consisted of 346 words and 422 rewriting rules. In the tests, the sentence recognition rate of 62.6% was obtained after speaker adaptation.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 2002년도 하계학술발표대회 논문집 제21권 1호
• /
• pp.175-178
• /
• 2002

본 논문에서는 Hidden Markov Model(HMM)을 이용하여 corpus 기반 TTS에 사용할 DB를 자동 음소 분할 해주는 시스템을 구현하였다. HMM을 이용해서 음소 분할 할 경우 HMM을 모델링 하는 방법에 따라 많은 성능의 차이가 난다. 따라서 본 논문에서는 HMM 모델링 방법에 따른 몇 가지 실험 및 성능 평가를 하였다. 실험 결과 음성 인식과는 달리 HMM모델링 시 triphone 모델보다 monophone 모델의 성능이 더 우수하였으며, 에너지 기반의 후처리를 통해 성능 향상을 얻을 수 있었다.

• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2003년도 5월 학술대회지
• /
• pp.168-171
• /
• 2003

This study is about modeling pronunciation dictionary necessary for PLU(phoneme like unit) level word recognition. The recognition of nonnative speakers' pronunciation enables an automatic diagnosis and an error detection which are the core of English pronunciation tutoring system. The above system needs two pronunciation dictionaries. One is for representing standard English pronunciation. The other is for representing Korean speakers' English Pronunciation. Both dictionaries are integrated to generate pronunciation networks for variants.

• 한국음향학회지
• /
• 제38권5호
• /
• pp.607-613
• /
• 2019

본 논문은 external knowledge를 사용한 lattice 없는 상호 정보 최대화(Lattice Free Maximum Mutual Information, LF-MMI) 기반 음향 모델링 방법을 제안한다. External knowledge란 음향 모델에서 사용하는 학습 데이터 이외의 문자열 데이터를 말한다. LF-MMI란 심층 신경망(Deep Neural Network, DNN) 학습의 최적화를 위한 목적 함수의 일종으로, 구별 학습에서 높은 성능을 보인다. LF-MMI에는 DNN의 사후 확률을 계산하기 위해 음소의 열을 사전 확률로 갖는다. 본 논문에서는 LF-MMI의 목적식의 사전 확률을 담당하는 음소 모델링에 external knowlege를 사용함으로써 과적합의 가능성을 낮추고, 음향 모델의 성능을 높이는 방법을 제안한다. External memory를 사용하여 사전 확률을 생성한 LF-MMI 모델을 사용했을 때 기존 LF-MMI와 비교하여 14 %의 상대적 성능 개선을 보였다.

• 한국음향학회지
• /
• 제22권1호
• /
• pp.23-27
• /
• 2003

최근 연속 음성 인식에서의 성능 향상을 위해 음절을 인식 단위로 사용하고자 하는 노력들이 보고되고 있다. 그러나 음절의 경우 음소에 비해 학습성이 음소에 비해 좋지 않고, 모델의 수가 음소에 비해 매우 많으므로 음절 경계에서의 문맥 종속 모델링이 어렵다는 단점을 갖고 있다. 본 논문에서는 한국어에서의 음절의 학습성을 향상시키기 위한 방법과 음절경계에서의 음소 문맥 종속 음절 모델링을 제안한다. 제안된 방법을 단어 인식 실험에 적용한 결과, 기존의 음절 모델과 비교하여 평균 46.23%의 에러 감소율을 보였다 우측 음소 종속 음절 모델 (right phone dependent syllable model)의 경우 트라이폰(triphone) 모델에 비해 16.7%의 에러 감소율을 볼 수 있었다.

• 한국음향학회지
• /
• 제9권3호
• /
• pp.42-47
• /
• 1990

음성처리 및 인식기기의 기능을 향상시키기 위해서는 생체공학적인 방법을 이용한 인체의 청각신경 정보처리 시스템의 연구가 중요하다. 그래서 본 논문에서는 와우각의 메카니즘을 분석한 기저막의 IIR 디지털 필터 모델링이 연구되었다. 특히 음소검출필터와 측징 추출을 위한 변별기능을 이용한 자음인식의 다층신경 모델을 구성한다. 이 모델은 자음인식에 있어서 90% 이상의 높은 감지율을 나타내고 있다.