• Title/Summary/Keyword: eigenspace

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Fast Speaker Adaptation and Environment Compensation Based on Eigenspace-based MLLR (Eigenspace-based MLLR에 기반한 고속 화자적응 및 환경보상)

  • Song Hwa-Jeon;Kim Hyung-Soon
    • 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.

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Neural Network-based place localization for a mobile Robot using eigenspace (Eigenspace를 이용한 신경회로망 기반의 로봇 위치 인식 시스템)

  • Lee, Hui-Seong;Lee, Yun-Hui;Kim, Eun-Tae;Park, Min-Yong
    • Proceedings of the KIEE Conference
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    • 2003.11c
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    • pp.1010-1013
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    • 2003
  • This paper describes an algorithm for determining robot location using appearance-based paradigm. This algorithm compress the image set using PCA(principal component analysis) to obtain a low-dimensional subspace, called the eigenspace, and it makes a manifold that represent a continuous-appearance function. To determine robot location, given an unknown input image, the recognition system first projects the image to eigenspace. Neural network use coefficients of the eigenspace to estimate the location of the mobile robot. The algorithm has been implemented and tested on a mobile robot system. In several trials it computes location accurately.

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Robust Feature Normalization Scheme Using Separated Eigenspace in Noisy Environments (분리된 고유공간을 이용한 잡음환경에 강인한 특징 정규화 기법)

  • Lee Yoonjae;Ko Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.24 no.4
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    • pp.210-216
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    • 2005
  • We Propose a new feature normalization scheme based on eigenspace for achieving robust speech recognition. In general, mean and variance normalization (MVN) is Performed in cepstral domain. However, another MVN approach using eigenspace was recently introduced. in that the eigenspace normalization Procedure Performs normalization in a single eigenspace. This Procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In this method. 39 dimensional feature distribution is represented using only a single eigenspace. However it is observed to be insufficient to represent all data distribution using only a sin91e eigenvector. For more specific representation. we apply unique na independent eigenspaces to cepstra, delta and delta-delta cepstra respectively in this Paper. We also normalize training data in eigenspace and get the model from the normalized training data. Finally. a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial recognition improvement over the basic eigenspace normalization.

Representing Human Motions in an Eigenspace Based on Surrounding Cameras

  • Houman, Satoshi;Rahman, M. Masudur;Tan, Joo Kooi;Ishikawa, Seiji
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.1808-1813
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    • 2004
  • Recognition of human motions using their 2-D images has various applications. An eigenspace method is employed in this paper for representing and recognizing human motions. An eigenspace is created from the images taken by multiple cameras that surround a human in motion. Image streams obtained from the cameras compose the same number of curved lines in the eigenspace and they are used for recognizing a human motion in a video image. Performance of the proposed technique is shown experimentally.

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Registration Error Compensation for Face Recognition Using Eigenface (Eigenface를 이용한 얼굴인식에서의 영상등록 오차 보정)

  • Moon Ji-Hye;Lee Byung-Uk
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.5C
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    • pp.364-370
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    • 2005
  • The first step of face recognition is to align an input face picture with database images. We propose a new algorithm of removing registration error in eigenspace. Our algorithm can correct for translation, rotation and scale changes. Linear matrix modeling of registration error enables us to compensate for subpixel errors in eigenspace. After calculating derivative of a weighting vector in eigenspace we can obtain the amount of translation or rotation without time consuming search. We verify that the correction enhances the recognition rate dramatically.

Localization of a mobile robot using the appearance-based approach (외향 기반 환경 인식을 사용한 이동 로봇의 위치인식 알고리즘)

  • 이희성;김은태
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.41 no.6
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    • pp.47-53
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    • 2004
  • This paper proposes an algerian for determining robot location using appearance-based paradigm. First, this algorithm compresses the image set using Principal Component Analysis(PCA) to obtain a low-dimensional subspace, called the eigenspace, and it makes a manifold that represent a continuous-appearance function. Neural network is employed to estimate the location of the mobile robot from the coefficients of the eigenspace. Then, Kalman filtering scheme is used for the fine estimation of the robot location. The algorithm has been implemented and tested on a mobile robot system. It is shown that the robot location is estimated accurately in several trials.

A novel robot localization algorithm based on neural network and Kalman filter (신경 회로망과 칼만 필터를 결합한 새로운 방식의 로봇 위치인식 알고리즘)

  • 이희성;김은태;박민용
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.519-522
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    • 2004
  • 본 논문에서는 외향 기반 접근법을 기반으로 한 로봇의 위치 추정 알고리즘을 제안한다. 로봇이 작업을 수행할 공간에서 강한 상관관계를 갖는 영상들을 취득하여 eigenspace로 투영 시킴으로써 주성분의 추출을 수행한다. 이 추출된 주성분은 신경 회로망을 이용해 eigenspace에서의 연속 외향 함수(continuous appearance function)로 나타낼 수 있다. 로봇의 위치 추정을 위해 새로운 영상이 주어지면 이것을 eigenspace로 투영 시킨 후 연속 외향 함수를 통해 로봇의 현재 위치를 추정한다. 최종적으로는, 영상안의 데이터에 칼만 필터를 적용함으로써 로봇의 정확한 위치와 영상으로 획득된 정보 사이의 오차를 이용하여 보다 정확한 이동 로봇의 위치를 추정하는 알고리즘을 제안한다.

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A Study On Text Independent Speaker Recognition Using Eigenspace (고유영역을 이용한 문자독립형 화자인식에 관한 연구)

  • 함철배;이동규;이두수
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.671-674
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    • 1999
  • We report the new method for speaker recognition. Until now, many researchers have used HMM (Hidden Markov Model) with cepstral coefficient or neural network for speaker recognition. Here, we introduce the method of speaker recognition using eigenspace. This method can reduce the training and recognition time of speaker recognition system. In proposed method, we use the low rank model of the speech eigenspace. In experiment, we obtain good recognition result.

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Fast Speaker Adaptation Based on Eigenspace-based MLLR Using Artificially Distorted Speech in Car Noise Environment (차량 잡음 환경에서 인위적 왜곡 음성을 이용한 Eigenspace-based MLLR에 기반한 고속 화자 적응)

  • Song, Hwa-Jeon;Jeon, Hyung-Bae;Kim, Hyung-Soon
    • Phonetics and Speech Sciences
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    • v.1 no.4
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    • pp.119-125
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    • 2009
  • This paper proposes fast speaker adaptation method using artificially distorted speech in telematics terminal under the car noise environment based on eigenspace-based maximum likelihood linear regression (ES-MLLR). The artificially distorted speech is built from adding the various car noise signals collected from a driving car to the speech signal collected from an idling car. Then, in every environment, the transformation matrix is estimated by ES-MLLR using the artificially distorted speech corresponding to the specific noise environment. In test mode, an online model is built by weighted sum of the environment transformation matrices depending on the driving condition. In 3k-word recognition task in the telematics terminal, we achieve a performance superior to ES-MLLR even using the adaptation data collected from the driving condition.

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Combining Empirical Feature Map and Conjugate Least Squares Support Vector Machine for Real Time Image Recognition : Research with Jade Solution Company

  • Kim, Byung Joo
    • International Journal of Internet, Broadcasting and Communication
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    • v.9 no.1
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    • pp.9-17
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    • 2017
  • This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.