• Title/Summary/Keyword: Phoneme Error

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Vocabulary Recognition Post-Processing System using Phoneme Similarity Error Correction (음소 유사율 오류 보정을 이용한 어휘 인식 후처리 시스템)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.7
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    • pp.83-90
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    • 2010
  • In vocabulary recognition system has reduce recognition rate unrecognized error cause of similar phoneme recognition and due to provided inaccurate vocabulary. Input of inaccurate vocabulary by feature extraction case of recognition by appear result of unrecognized or similar phoneme recognized. Also can't feature extraction properly when phoneme recognition is similar phoneme recognition. In this paper propose vocabulary recognition post-process error correction system using phoneme likelihood based on phoneme feature. Phoneme likelihood is monophone training phoneme data by find out using MFCC and LPC feature extraction method. Similar phoneme is induced able to recognition of accurate phoneme due to inaccurate vocabulary provided unrecognized reduced error rate. Find out error correction using phoneme likelihood and confidence when vocabulary recognition perform error correction for error proved vocabulary. System performance comparison as a result of recognition improve represent MFCC 7.5%, LPC 5.3% by system using error pattern and system using semantic.

Reliability measure improvement of Phoneme character extract In Out-of-Vocabulary Rejection Algorithm (미등록어 거절 알고리즘에서 음소 특성 추출의 신뢰도 측정 개선)

  • Oh, Sang-Yeob
    • Journal of Digital Convergence
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    • v.10 no.6
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    • pp.219-224
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    • 2012
  • In the communication mobile terminal, Vocabulary recognition system has low recognition rates, because this problems are due to phoneme feature extract from inaccurate vocabulary. Therefore they are not recognize the phoneme and similar phoneme misunderstanding error. To solve this problem, this paper propose the system model, which based on the two step process. First, input phoneme is represent by number which measure the distance of phonemes through phoneme likelihood process. next step is recognize the result through the reliability measure. By this process, we minimize the phoneme misunderstanding error caused by inaccurate vocabulary and perform error correction rate for error provrd vocabulary using phoneme likelihood and reliability. System performance comparison as a result of recognition improve represent 2.7% by method using error pattern learning and semantic pattern.

A Study on Error Correction Using Phoneme Similarity in Post-Processing of Speech Recognition (음성인식 후처리에서 음소 유사율을 이용한 오류보정에 관한 연구)

  • Han, Dong-Jo;Choi, Ki-Ho
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.6 no.3
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    • pp.77-86
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    • 2007
  • Recently, systems based on speech recognition interface such as telematics terminals are being developed. However, many errors still exist in speech recognition and then studies about error correction are actively conducting. This paper proposes an error correction in post-processing of the speech recognition based on features of Korean phoneme. To support this algorithm, we used the phoneme similarity considering features of Korean phoneme. The phoneme similarity, which is utilized in this paper, rams data by mono-phoneme, and uses MFCC and LPC to extract feature in each Korean phoneme. In addition, the phoneme similarity uses a Bhattacharrya distance measure to get the similarity between one phoneme and the other. By using the phoneme similarity, the error of eo-jeol that may not be morphologically analyzed could be corrected. Also, the syllable recovery and morphological analysis are performed again. The results of the experiment show the improvement of 7.5% and 5.3% for each of MFCC and LPC.

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Phoneme Similarity Error Correction System using Bhattacharyya Distance Measurement Method (바타챠랴 거리 측정법을 이용한 음소 유사율 오류 보정 개선 시스템)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.6
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    • pp.73-80
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    • 2010
  • Vocabulary recognition system is providing inaccurate vocabulary and similar phoneme recognition due to reduce recognition rate. It's require method of similar phoneme recognition unrecognized and efficient feature extraction process. Therefore in this paper propose phoneme likelihood error correction improvement system using based on phoneme feature Bhattacharyya distance measurement. Phoneme likelihood is monophone training data phoneme using HMM feature extraction method, similar phoneme is induced recognition able to accurate phoneme using Bhattacharyya distance measurement. They are effective recognition rate improvement. System performance comparison as a result of recognition improve represent 1.2%, 97.91% by Euclidean distance measurement and dynamic time warping(DTW) system.

Key-word Recognition System using Signification Analysis and Morphological Analysis (의미 분석과 형태소 분석을 이용한 핵심어 인식 시스템)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of Korea Multimedia Society
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    • v.13 no.11
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    • pp.1586-1593
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    • 2010
  • Vocabulary recognition error correction method has probabilistic pattern matting and dynamic pattern matting. In it's a sentences to based on key-word by semantic analysis. Therefore it has problem with key-word not semantic analysis for morphological changes shape. Recognition rate improve of vocabulary unrecognized reduced this paper is propose. In syllable restoration algorithm find out semantic of a phoneme recognized by a phoneme semantic analysis process. Using to sentences restoration that morphological analysis and morphological analysis. Find out error correction rate using phoneme likelihood and confidence for system parse. When vocabulary recognition perform error correction for error proved vocabulary. system performance comparison as a result of recognition improve represent 2.0% by method using error pattern learning and error pattern matting, vocabulary mean pattern base on method.

A New Speaker Adaptation Technique using Maximum Model Distance

  • Tahk, Min-Jea
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.154.2-154
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    • 2001
  • This paper presented a adaptation approach based on maximum model distance (MMD) method. This method shares the same framework as they are used for training speech recognizers with abundant training data. The MMD method could adapt to all the models with or without adaptation data. If large amount of adaptation data is available, these methods could gradually approximate the speaker-dependent ones. The approach is evaluated through the phoneme recognition task on the TIMIT corpus. On the speaker adaptation experiments, up to 65.55% phoneme error reduction is achieved. The MMD could reduce phoneme error by 16.91% even when ...

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A New Speaker Adaptation Technique using Maximum Model Distance

  • Lee, Man-Hyung;Hong, Suh-Il
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.99.1-99
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    • 2001
  • This paper presented an adaptation approach based on maximum model distance (MMD) method. This method shares the same framework as they are used for training speech recognizers with abundant training data. The MMD method could adapt to all the models with or without adaptation data. If large amount of adaptation data is available, these methods could gradually approximate the speaker-dependent ones. The approach is evaluated through the phoneme recognition task on the TIMIT corpus. On the speaker adaptation experiments, up to 65.55% phoneme error reduction is achieved. The MMD could reduce phoneme error by 16.91% even when only one adaptation utterance is used.

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Analysis of Phonemic Errors of Korean Learners According to Language and Proficiency (언어권과 숙달도에 따른 한국어 학습자의 발음 오류 분석 - 음소 오류를 중심으로 -)

  • 유소영;강현화
    • Language Facts and Perspectives
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    • v.44
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    • pp.357-397
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    • 2018
  • The purpose of this paper is to investigate the phonemic errors in Korean learner's spoken corpus. Through this, we tried to investigate the common errors and the errors in certain languages. The results of the analysis were as follows. First, Errors that distinguish three phonemes(plain sound, tense sound, aspiration sound) were high in all languages. In the middle phonemes, the most common errors in pronouncing 'ㅓ' in all languages. Second, the errors of each language are different. Comparing the ratios by position, Chinese characters had the most common errors with 50% in final phoneme, and the Japanese language showed equal errors in initial, middle, and end. In English, initial phoneme errors accounted for 58%. Vietnamese Learners showed intensive errors in the initial and final phoneme. Third, in addition to the phoneme errors, we also examined the allophone errors and foreign language pronunciation errors. The allophone errors are mainly concentrated in 'ㄹ', ​​and the pronunciation of the foreign language is mainly used in the source language or the native language of the learners. This paper analyzes the phoneme errors in the Learner's spoken language through the spoken corpus data with representative and annotation consistency. Through this study, we could compare the difference of phoneme errors of Main Korean learners.

Key-word Error Correction System using Syllable Restoration Algorithm (음절 복원 알고리즘을 이용한 핵심어 오류 보정 시스템)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.10
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    • pp.165-172
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    • 2010
  • There are two method of error correction in vocabulary recognition system. one error pattern matting base on method other vocabulary mean pattern base on method. They are a failure while semantic of key-word problem for error correction. In improving, in this paper is propose system of key-word error correction using algorithm of syllable restoration. System of key-word error correction by processing of semantic parse through recognized phoneme meaning. It's performed restore by algorithm of syllable restoration phoneme apply fluctuation before word. It's definitely parse of key-word and reduced of unrecognized. Find out error correction rate using phoneme likelihood and confidence for system parse. When vocabulary recognition perform error correction for error proved vocabulary. system performance comparison as a result of recognition improve represent 2.3% by method using error pattern learning and error pattern matting, vocabulary mean pattern base on method.

Speech Recognition Error Compensation using MFCC and LPC Feature Extraction Method (MFCC와 LPC 특징 추출 방법을 이용한 음성 인식 오류 보정)

  • Oh, Sang-Yeob
    • Journal of Digital Convergence
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    • v.11 no.6
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    • pp.137-142
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    • 2013
  • Speech recognition system is input of inaccurate vocabulary by feature extraction case of recognition by appear result of unrecognized or similar phoneme recognized. Therefore, in this paper, we propose a speech recognition error correction method using phoneme similarity rate and reliability measures based on the characteristics of the phonemes. Phonemes similarity rate was phoneme of learning model obtained used MFCC and LPC feature extraction method, measured with reliability rate. Minimize the error to be unrecognized by measuring the rate of similar phonemes and reliability. Turned out to error speech in the process of speech recognition was error compensation performed. In this paper, the result of applying the proposed system showed a recognition rate of 98.3%, error compensation rate 95.5% in the speech recognition.