• Speech Sciences
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• v.8 no.4
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• pp.17-33
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• 2001

In this paper, we implement the large variable vocabulary speech recognition system, which is characterized by no additional pre-training process and no limitation of recognized word list. We have designed the system in order to achieve the high recognition rate using the decision tree based state tying algorithm and in order to reduce the processing time using the gaussian selection based variable flooring algorithm, the limitation algorithm of the number of nodes and ENNS algorithm. The gaussian selection based variable flooring algorithm shows that it can reduce the total processing time by more than half of the recognition time, but it brings about the reduction of recognition rate. In other words, there is a trade off between the recognition rate and the processing time. The limitation algorithm of the number of nodes shows the best performance when the number of gaussian mixtures is a three. Both of the off-line and on-line experiments show the same performance. In our experiments, there are some differences of the recognition rate and the average recognition time according to the distinction of genders, speakers, and the number of vocabulary.

• English Language & Literature Teaching
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• v.18 no.1
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• pp.171-195
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• 2012

While noticing that there is insufficient interest in the assessment of EFL learners' vocabulary levels or sizes, the researchers developed two tests identical in form (Forms A and B) to assess the lexical knowledge of Korean university learners at the $1^{st}{\sim}10^{th}$ 1,000 word bands by adapting a pre-established vocabulary levels test (VLT). Of equal concern was to investigate if the VLT was equally a valid and reliable instrument to be used on measuring the lexical knowledge of EFL learners. The participants were 804 university freshmen enrolled in a General Education English Course from four different colleges. The learners were asked to respond to either Form A or B. While scores generally fell towards the lower frequency bands, multiple regression found the Korean College Scholastic Ability Test (CSAT) to be a significant variable for predicting the learners' vocabulary sizes. From a methodological perspective, however, noticeable differences between Forms A and B could be found with item response theory analysis. The findings of the study provide suggestions on how future VLT for testing EFL learners may have to be redesigned.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1471-1479
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• 2008

In this paper, we propose a technique to improve Out-Of-Vocabulary(OOV) rejection algorithms in variable vocabulary recognition system which is much used in ASR(Automatic Speech Recognition). The rejection system can be classified into two categories by their implementation method, keyword spotting method and utterance verification method. The utterance verification method uses the likelihood ratio of each phoneme Viterbi score relative to anti-phoneme score for deciding OOV. In this paper, we add speaker verification system before utterance verification and calculate an speaker verification probability. The obtained speaker verification probability is applied for determining the proposed variable-confidence threshold. Using the proposed method, we achieve the significant performance improvement; CA(Correctly Accepted for keyword) 94.23%, CR(Correctly Rejected for out-of-vocabulary) 95.11% in office environment, and CA 91.14%, CR 92.74% in noisy environment.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.369-372
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• 1998

본 논문은 어휘독립(Vocabulary-Independent) 환경에서 별도의 훈련과정 없이 인식대상 어휘를 추가 및 변경할 수 있는 가변어휘(Variable Vocabulary) 음성인식에 관한 연구를 다룬다. 가변어휘 인식은 처음에 대용량 음성 데이터베이스(DB)로 음소모델을 훈련하고 인식대상 어휘가 결정되면 발음사전에 의거하여 음소모델을 연결함으로써 별도의 훈련과정 없이 인식대상 어휘를 변경 및 추가할 수 있다. 문맥 종속형(Context-Dependent) 음소 모델인 triphone을 사용하여 인식실험을 하였고, 인식성능의 비교를 위해 어휘종속 모델을 별도로 구성하여 인식실험을 하였다. Unseen triphone 문제와 훈련 DB의 부족으로 인한 모델 파라메터의 신뢰성 저하를 방지하기 위해 state-tying 방법 중 음성학적 지식에 기반을 둔 tree-based clustering(TBC) 기법[1]을 도입하였다. Mel Frequency Cepstrum Coefficient(MFCC)와 대수에너지에 기반을 둔 3 가지 음성특징 벡터를 사용하여 인식 실험을 병행하였고, 연속 확률분포를 가지는 Hidden Markov Model(HMM) 기반의 고립단어 인식시스템을 구현하였다. 인식 실험에는 22 개 부서명 DB[3]를 사용하였다. 실험결과 어휘독립 환경에서 최고 98.4%의 인식률이 얻어졌으며, 어휘종속 환경에서의 인식률 99.7%에 근접한 성능을 보였다.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.31-35
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• 1997

In this paper, we propose a variable vocabulary word recognizer that is able to recognize new words not exist in training data. For the variable vocabulary word recognizer, we must have an on-line lexicon generator to transform new candidate words to the corresponding pronunciation sequences of phones without any large lexicon table. And, we also must make outputs. In order to model the phones and allophones reliably, we define Korean allophones by triphone clustering based on phonetic knowledge of preceding and succeeding phones of each phone. Using the clustering method, we generated 1,548 allophones with POW (Phonetically Optimized Words) 3,848 word DB. We evaluated the proposed word recognizer with POW 3,848 DB, PBW (Phonetically Balanced Words) 445 DB, and 244 word DB in hotel reservation task. Experimental results showed word recognition accuracy of 79.6% for the POW DB corresponding to vocabulary-dependent case, 79.4% in case of 445 word lexicon and 88.9% in case of 100 word lexicon for the PBW DB, and 71.4% for the hotel reservation DB corresponding to vocabulary-independent case.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2
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• pp.48-52
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• 1999

In this paper, we describe a Voice Command Web Browser using a variable vocabulary word recognizer that can do Internet surfing with Korean speech recognition on the Web. The feature of this browser is that it can handle the links and menus of the web browser by speech. Therefore, we can use speech interface together with mouse for web browsing. To recognize the recognition candidates dynamically changing according to Web pages, we use the variable vocabulary word recognizer. The recognizer was trained using POW (Phonetically Optimized Words) 3,848 words. So that it can recognize new words which did not exist in training data. The preliminary test results showed that the performance of speaker-independent and vocabulary-independent recognition is 93.8% for 32 Korean words. The Voice Command Web Browser was developed on windows 95/NT using Netscape Navigator and reflected usability test results in order to offer easy interface to users unfamiliar with speech interface. In on-line experiment of speaker-independent and environment-independent situation, Voice Command Web Browser showed recognition accuracy of 90%.

• The Journal of the Acoustical Society of Korea
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• v.20 no.2
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• pp.27-34
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• 2001

Utterance verification is used in variable vocabulary word recognition to reject the word that does not belong to in-vocabulary word or does not belong to correctly recognized word. Utterance verification is an important technology to design a user-friendly speech recognition system. We propose a new utterance verification algorithm for no-training utterance verification system based on the minimum verification error. First, using PBW (Phonetically Balanced Words) DB (445 words), we create no-training anti-phoneme models which include many PLUs(Phoneme Like Units), so anti-phoneme models have the minimum verification error. Then, for OOV (Out-Of-Vocabulary) rejection, the phoneme-based confidence measure which uses the likelihood between phoneme model (null hypothesis) and anti-phoneme model (alternative hypothesis) is normalized by null hypothesis, so the phoneme-based confidence measure tends to be more robust to OOV rejection. And, the word-based confidence measure which uses the phoneme-based confidence measure has been shown to provide improved detection of near-misses in speech recognition as well as better discrimination between in-vocabularys and OOVs. Using our proposed anti-model and confidence measure, we achieve significant performance improvement; CA (Correctly Accept for In-Vocabulary) is about 89％, and CR (Correctly Reject for OOV) is about 90％, improving about 15-21％ in ERR (Error Reduction Rate).

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.3-8
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• 1999

Previous variable vocabulary speech recognition systems with context-independent acoustic modeling, could not represent the effect of neighboring phonemes. To solve this problem, we use allophone-based context-dependent acoustic model. This paper describes the method to improve acoustic model of the system effectively. Acoustic model is improved by using allophone clustering technique that uses entropy as a similarity measure and the optimal allophone model is generated by changing the number of allophones. We evaluate performance of the improved system by using Phonetically Optimized Words(POW) DB and PC commands(PC) DB. As a result, the allophone model composed of six hundreds allophones improved the recognition rate by 13% from the original context independent model m POW test DB.

• The Journal of the Acoustical Society of Korea
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• v.25 no.1E
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• pp.20-24
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• 2006

In this paper, we focused on the real time implementation of a speech recognition system with medium size of vocabulary considering its application to a mobile phone. First, we developed the PC based variable vocabulary word recognizer having the size of program memory and total acoustic models as small as possible. To reduce the memory size of acoustic models, linear discriminant analysis and phonetic tied mixture were applied in the feature selection process and training HMMs, respectively. In addition, state based Gaussian selection method with the real time cepstral normalization was used for reduction of computational load and robust recognition. Then, we verified the real-time operation of the implemented recognition system on the TMS320C6201 EVM board. The implemented recognition system uses memory size of about 610 kbytes including both program memory and data memory. The recognition rate was 95.86% for ETRI 445DB, and 96.4%, 97.92%, 87.04% for three kinds of name databases collected through the mobile phones.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.56-59
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• 1998

POW 3848 DB 및 SNR 이 크게 다른 2 종류의 PC168 DB를 대상으로 가변어휘 음성인식 시스템을 이용하여 훈련 및 성능 평가 실험을 수행한 내용에 대해서 기술하고 있다. 실험의 목적은 위의 3종류의 DB를 조합하여 얻은 DB 환경하에서 인식기를 훈련시키면서, DB 의 조합 및 훈련방법에 따른 인식기의 성능과의 상관관계를 도출하고자 하였다. DB 의 조합은 POW DB 와 SNR 이 높은 PC DB , 및 3종류의 DB 모두로 구성하였다. 인식기는 40개의 음소로 구성된 문맥 독립형 SCHMM 모델이며, 각 음소당 3개의 상태로 이루어져 있다. 실험 결과, 대부분의 경우에서 ITERATION이 1.0인 경우에 최고 인식률을 나타내고 있으며, INTERATION 이 3.0 이상인 경우에는 항상 CASE 3의 실험방법이 우세한 결과를 나타내었다. 또한 CASE 1으로 훈련한 경우가 CASE 2 보다는 각각의 실험 DB 에 대해서 대체적으로 좋은 결과를 보였다.