• Title, Summary, Keyword: Feature compensation

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Feature Compensation Combining SNR-Dependent Feature Reconstruction and Class Histogram Equalization

  • Suh, Young-Joo;Kim, Hoi-Rin
    • ETRI Journal
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    • v.30 no.5
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    • pp.753-755
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    • 2008
  • In this letter, we propose a new histogram equalization technique for feature compensation in speech recognition under noisy environments. The proposed approach combines a signal-to-noise-ratio-dependent feature reconstruction method and the class histogram equalization technique to effectively reduce the acoustic mismatch present in noisy speech features. Experimental results from the Aurora 2 task confirm the superiority of the proposed approach for acoustic feature compensation.

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Comparison of the recognition performance of Korean connected digit telephone speech depending on channel compensation methods and feature parameters (채널보상기법 및 특징파라미터에 따른 한국어 연속숫자음 전화음성의 인식성능 비교)

  • Jung Sung Yun;Kim Min Sung;Son Jong Mok;Bae Keun Sung;Kim Sang Hun
    • Proceedings of the KSPS conference
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    • pp.201-204
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    • 2002
  • As a preliminary study for improving recognition performance of the connected digit telephone speech, we investigate feature parameters as well as channel compensation methods of telephone speech. The CMN and RTCN are examined for telephone channel compensation, and the MFCC, DWFBA, SSC and their delta-features are examined as feature parameters. Recognition experiments with database we collected show that in feature level DWFBA is better than MFCC and for channel compensation RTCN is better than CMN. The DWFBA+Delta_ Mel-SSC feature shows the highest recognition rate.

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Incorporation of IMM-based Feature Compensation and Uncertainty Decoding (IMM 기반 특징 보상 기법과 불확실성 디코딩의 결합)

  • Kang, Shin-Jae;Han, Chang-Woo;Kwon, Ki-Soo;Kim, Nam-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37 no.6C
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    • pp.492-496
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    • 2012
  • This paper presents a decoding technique for speech recognition using uncertainty information from feature compensation method to improve the speech recognition performance in the low SNR condition. Traditional feature compensation algorithms have difficulty in estimating clean feature parameters in adverse environment. Those algorithms focus on the point estimation of desired features. The point estimation of feature compensation method degrades speech recognition performance when incorrectly estimated features enter into the decoder of speech recognition. In this paper, we apply the uncertainty information from well-known feature compensation method, such as IMM, to the recognition engine. Applied technique shows better performance in the Aurora-2 DB.

Robust Speech Recognition Using Weighted Auto-Regressive Moving Average Filter (가중 ARMA 필터를 이용한 강인한 음성인식)

  • Ban, Sung-Min;Kim, Hyung-Soon
    • Phonetics and Speech Sciences
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    • v.2 no.4
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    • pp.145-151
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    • 2010
  • In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.

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Spectral Feature Transformation for Compensation of Microphone Mismatches

  • Jeong, So-Young;Oh, Sang-Hoon;Lee, Soo-Young
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.4E
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    • pp.150-154
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    • 2003
  • The distortion effects of microphones have been analyzed and compensated at mel-frequency feature domain. Unlike popular bias removal algorithms a linear transformation of mel-frequency spectrum is incorporated. Although a diagonal matrix transformation is sufficient for medium-quality microphones, a full-matrix transform is required for low-quality microphones with severe nonlinearity. Proposed compensation algorithms are tested with HTIMIT database, which resulted in about 5 percents improvements in recognition rate over conventional CMS algorithm.

Speaker Identification Using an Ensemble of Feature Enhancement Methods (특징 강화 방법의 앙상블을 이용한 화자 식별)

  • Yang, IL-Ho;Kim, Min-Seok;So, Byung-Min;Kim, Myung-Jae;Yu, Ha-Jin
    • Phonetics and Speech Sciences
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    • v.3 no.2
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    • pp.71-78
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    • 2011
  • In this paper, we propose an approach which constructs classifier ensembles of various channel compensation and feature enhancement methods. CMN and CMVN are used as channel compensation methods. PCA, kernel PCA, greedy kernel PCA, and kernel multimodal discriminant analysis are used as feature enhancement methods. The proposed ensemble system is constructed with the combination of 15 classifiers which include three channel compensation methods (including 'without compensation') and five feature enhancement methods (including 'without enhancement'). Experimental results show that the proposed ensemble system gives highest average speaker identification rate in various environments (channels, noises, and sessions).

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Speech Enhancement Based on Feature Compensation for Independently Applying to Different Types of Speech Recognition Systems (이기종 음성 인식 시스템에 독립적으로 적용 가능한 특징 보상 기반의 음성 향상 기법)

  • Kim, Wooil
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.10
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    • pp.2367-2374
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    • 2014
  • This paper proposes a speech enhancement method which can be independently applied to different types of speech recognition systems. Feature compensation methods are well known to be effective as a front-end algorithm for robust speech recognition in noisy environments. The feature types and speech model employed by the feature compensation methods should be matched with ones of the speech recognition system for their effectiveness. However, they cannot be successfully employed by the speech recognition with "unknown" specification, such as a commercialized speech recognition engine. In this paper, a speech enhancement method is proposed, which is based on the PCGMM-based feature compensation method. The experimental results show that the proposed method significantly outperforms the conventional front-end algorithms for unknown speech recognition over various background noise conditions.

Performance Comparison of Korean Connected Digit Telephone Speech Recognition According to Aurora Feature Extraction (Aurora 특징파라미터 추출기법에 따른 한국어 연속숫자음 전화음성의 인식 성능 비교)

  • Kim Min Sung;Jung Sung Yun;Son Jong Mok;Bae Keun Sung;Kim Sang Hun
    • Proceedings of the KSPS conference
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    • pp.145-148
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    • 2003
  • To improve the recognition performance of Korean connected digit telephone speech, in this paper, both Aurora feature extraction method that employs noise reduction 2-state Wiener filter and DWFBA method are investigated and used. CMN and MRTCN are applied to static features for channel compensation. Telephone digit speech database released by SITEC is used for recognition experiments with HTK system. Experimental results has shown that Aurora feature is slightly better than MFCC and DWFBA without channel compensation. And when channel compensation is included, Aurora feature is slightly better than DWFBA with MRTCN.

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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.