• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.82-91
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• 1991

The history of automatic speech recognition research, and current and future speech products at Fujitsu are introduced here. The speech recognition research at Fujitsu started in 1970. Our research efforts have results in the production of a speaker dependent 12,000 word discrete / connected word recognizer(F2360), and a speaker independent 17 word discrete word recognizer(F2355L/S). Currently, we are working on a larger vocabulary speech recognizer, in which an input utterance will be matched with networks representing possible phonemic variations. Its application to text input is also discussed.

• The Journal of the Acoustical Society of Korea
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• v.16 no.6
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• pp.106-109
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• 1997

For the practical isolated word recognition system, the ability to reject the out-of -vocabulary(OOV) is required. In this paper, we present a rejection method which uses the clustered phoneme modeling combined with postprocessing by likelihood ratio scoring. Our baseline speech recognition system was based on the whole-word continuous HMM. And 6 clustered phoneme models were generated using statistical method from the 45 context independent phoneme models, which were trained using the phonetically balanced speech database. The test of the rejection performance for speaker independent isolated words recogntion task on the 22 section names shows that our method is superior to the conventional postprocessing method, performing the rejection according to the likelihood difference between the first and second candidates. Furthermore, this clustered phoneme models do not require retraining for the other isolated word recognition system with different vocabulary sets.

• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.65-68
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• 1995

이 논문은 현재 주식시장에 상장되어 있는 약 700개 회사의 현재주가를 음성인식을 이용하여 검색할 수 있는 대어휘, 화자독립, 단어독립 음성인식 시스팀의 운용자를 위한 운용관리 시스팀에 대해 기술하였다. KT-STOCK은 시스팀의 음성안내에 따라 사용자가 전화기에 상장회사 이름을 말하면, 이 시스팀은 그 회사의 현재 증권정보를 말해준다. 이 시스팀의 운용관리 시스팀은 주식시장에 상장된 종목의 변화에 따라서 인식대상 단어를 추가하거나 삭제, 조회할 때 그 처리를 용이하게 할 수 있도록 구현되었다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.53-60
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• 1999

This paper is a study on the korean broadcasting speech recognition. Here we present the methods for the large vocabuary continuous speech recognition. Our main concerns are the language modeling and the search algorithm. The used acoustic model is the uni-phone semi-continuous hidden markov model and the used linguistic model is the N-gram model. The search algorithm consist of three phases in order to utilize all available acoustic and linguistic information. First, we use the forward Viterbi beam search to find word end frames and to estimate related scores. Second, we use the backword Viterbi beam search to find word begin frames and to estimate related scores. Finally, we use A/sup */ search to combine the above two results with the N-grams language model and to get recognition results. Using these methods maximum 96.0% word recognition rate and 99.2% syllable recognition rate are achieved for the speaker-independent continuous speech recognition problem with about 12,000 vocabulary size.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.3-9
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• 1999

This paper presents the study on the rejection capability based on anti-phone modeling for vocabulary independent speech recognition system. The rejection system detects and rejects out-of-vocabulary words which were not included in candidate words which are defined while the speech recognizer is made. The rejection system can be classified into two categories by their implementation methods, keyword spotting method and utterance verification method. The keyword spotting method uses an extra filler model as a candidate word as well as keyword models. The utterance verification method uses the anti-models for each phoneme for the calculation of confidence score after it has constructed the anti-models for all phonemes. We implemented an utterance verification algorithm which can be used for vocabulary independent speech recognizer. We also compared three kinds of means for the calculation of confidence score, and found out that the geometric mean had shown the best result. For the normalization of confidence score, usually Sigmoid function is used. On using it, we compared the effect of the weight constant for Sigmoid function and determined the optimal value. And we compared the effects of the size of cohort set, the results showed that the larger set gave the better results. And finally we found out optimal confidence score threshold value. In case of using the threshold value, the overall recognition rate including rejection errors was about 76%. This results are going to be adapted for stock information system based on speech recognizer which is currently provided as an experimental service by Korea Telecom.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.19 no.5
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• pp.3-8
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• 2000

In this paper, we studied design and implementation of a robust speech recognition system in noisy car environment. The reference pattern used in the system is DMS(Dynamic Multi-Section). Two separate acoustic models, which are selected automatically depending on the noisy car environment for the speech in a car moving at below 80km/h and over 80km/h are proposed. PLP(Perceptual Linear Predictive) of order 13 is used for the feature vector and OSDP (One-Stage Dynamic Programming) is used for decoding. The system also has the function of editing the phone-book for voice dialing. The system yields a recognition rate of 89.75% for male speakers in SI (speaker independent) mode in a car running on a cemented express way at over 80km/h with a vocabulary of 33 words. The system also yields a recognition rate of 92.29% for male speakers in SI mode in a car running on a paved express way at over 80km/h.

• The Journal of the Acoustical Society of Korea
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• v.10 no.2
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• pp.23-28
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• 1991

This paper is a study on isolated word recognition by independent speaker, we propose DTW speech recognition system by modified dynamic averaging method as reference pattern. 57 city names are selected as recognition vocabulary and 2th LPC cepstrum coefficients are used as the feature parameter. In this paper, besides recognition experiment using modified dynamic averaging method as reference pattern, we perform recognition experiments using causal method, dynamic averaging method, linear averaging method and clustering method with the same data in the same conditions for comparison with it. Through the experiment result, it is proved that recogntion rate by DTW using modified dynamic averaging method is the best as 97.6 percent.

• The Journal of the Acoustical Society of Korea
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• v.19 no.3
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• pp.51-56
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• 2000

Is this paper, we studied on the automatic speech control system in real-time windows environment using voice recognition. The applied reference pattern is the variable DMS model which is proposed to fasten execution speed and the one-stage DP algorithm using this model is used for recognition algorithm. The recognition vocabulary set is composed of control command words which are frequently used in windows environment. In this paper, an automatic speech period detection algorithm which is for on-line voice processing in windows environment is implemented. The variable DMS model which applies variable number of section in consideration of duration of the input signal is proposed. Sometimes, unnecessary recognition target word are generated. therefore model is reconstructed in on-line to handle this efficiently. The Perceptual Linear Predictive analysis method which generate feature vector from extracted feature of voice is applied. According to the experiment result, but recognition speech is fastened in the proposed model because of small loud of calculation. The multi-speaker-independent recognition rate and the multi-speaker-dependent recognition rate is 99.08% and 99.39% respectively. In the noisy environment the recognition rate is 96.25%.

• MALSORI
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• no.58
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• pp.35-44
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• 2006

Maximum likelihood linear regression (MLLR) adaptation experiences severe performance degradation with very tiny amount of adaptation data. Eigenspace- based MLLR, as an alternative to MLLR for fast speaker adaptation, also has a weak point that it cannot deal with the mismatch between training and testing environments. In this paper, we propose a simultaneous fast speaker and environment adaptation based on eigenspace-based MLLR. We also extend the sub-stream based eigenspace-based MLLR to generalize the eigenspace-based MLLR with bias compensation. A vocabulary-independent word recognition experiment shows the proposed algorithm is superior to eigenspace-based MLLR regardless of the amount of adaptation data in diverse noisy environments. Especially, proposed sub-stream eigenspace-based MLLR with bias compensation yields 67% relative improvement with 10 adaptation words in 10 dB SNR environment, in comparison with the conventional eigenspace-based MLLR.