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Christoph S Garbe - One of the best experts on this subject based on the ideXlab platform.

  • a statistical Confidence Measure for optical flows
    European Conference on Computer Vision, 2008
    Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S Garbe
    Abstract:

    Confidence Measures are crucial to the interpretation of any optical flow Measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated Confidence Measure for optical flow Measurements is still missing. We aim at filling this gap with this contribution, where such a Confidence Measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new Confidence Measure is computed from merely the results of the optical flow estimator and hence can be applied to any optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'Confidence' Measures.

  • ECCV (3) - A Statistical Confidence Measure for Optical Flows
    Lecture Notes in Computer Science, 2008
    Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S Garbe
    Abstract:

    Confidence Measures are crucial to the interpretation of any optical flow Measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated Confidence Measure for optical flow Measurements is still missing. We aim at filling this gap with this contribution, where such a Confidence Measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new Confidence Measure is computed from merely the results of the optical flow estimator and hence can be applied to any optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'Confidence' Measures.

  • an adaptive Confidence Measure for optical flows based on linear subspace projections
    DAGM conference on Pattern Recognition, 2007
    Co-Authors: Claudia Kondermann, Daniel Kondermann, Bernd Jahne, Christoph S Garbe
    Abstract:

    Confidence Measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used Confidence Measures, which have been found of at best intermediate quality. Hence, we propose a new Confidence Measure based on linear subspace projections. The results are compared to the best previously proposed Confidence Measures with respect to an optimal Confidence. Using the proposed Measure we are able to improve previous results by up to 31%.

  • DAGM-Symposium - An adaptive Confidence Measure for optical flows based on linear subspace projections
    Lecture Notes in Computer Science, 2007
    Co-Authors: Claudia Kondermann, Daniel Kondermann, Bernd Jahne, Christoph S Garbe
    Abstract:

    Confidence Measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used Confidence Measures, which have been found of at best intermediate quality. Hence, we propose a new Confidence Measure based on linear subspace projections. The results are compared to the best previously proposed Confidence Measures with respect to an optimal Confidence. Using the proposed Measure we are able to improve previous results by up to 31%.

Benjamin Lecouteux - One of the best experts on this subject based on the ideXlab platform.

  • a segment level Confidence Measure for spoken document retrieval
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Gregory Senay, Georges Linares, Benjamin Lecouteux
    Abstract:

    This paper presents a semantic Confidence Measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition (ASR) Confidence Measure and a Semantic Compacity Index (SCI), that estimates the relevance of the words considering the semantic context in which they occurred. Experiments are conducted on the French Broadcast news corpus ESTER, by simulating a classical SDR usage scenario: users submit text-queries to a search engine that is expected to return the most relevant documents regarding the query. Results demonstrate the interest of using semantic level information to predict the transcription indexability.

  • ICASSP - A segment-level Confidence Measure for Spoken Document Retrieval
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Gregory Senay, Georges Linares, Benjamin Lecouteux
    Abstract:

    This paper presents a semantic Confidence Measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition (ASR) Confidence Measure and a Semantic Compacity Index (SCI), that estimates the relevance of the words considering the semantic context in which they occurred. Experiments are conducted on the French Broadcast news corpus ESTER, by simulating a classical SDR usage scenario: users submit text-queries to a search engine that is expected to return the most relevant documents regarding the query. Results demonstrate the interest of using semantic level information to predict the transcription indexability.

Wayne H Ward - One of the best experts on this subject based on the ideXlab platform.

  • a concept graph based Confidence Measure
    International Conference on Acoustics Speech and Signal Processing, 2002
    Co-Authors: Kadri Hacioglu, Wayne H Ward
    Abstract:

    In this paper, the Confidence Measure of a hypothesized word is derived from its posterior probability. In contrast to common approaches, in which N-best lists or word graphs/lattices are used, the posterior probabilities are derived from a concept graph. The concept graph is obtained from a word graph through a partial parsing process using semantic grammars. This approach allows us to use relatively complex and better language models along with acoustic models to compute word posterior probabilities. The language model used is comprised of stochastic context free grammars (one for each concept) and an n-gram concept language model. We show that the posterior probabilities computed on concept graphs outperform those computed on word graphs when used as Confidence Measures. Results are presented within the context of Colorado University (CU) Communicator System; a telephone-based dialog system for making travel plans by accessing information about flights, hotels and car rentals.

  • ICASSP - A concept graph based Confidence Measure
    IEEE International Conference on Acoustics Speech and Signal Processing, 2002
    Co-Authors: Kadri Hacioglu, Wayne H Ward
    Abstract:

    In this paper, the Confidence Measure of a hypothesized word is derived from its posterior probability. In contrast to common approaches, in which N-best lists or word graphs/lattices are used, the posterior probabilities are derived from a concept graph. The concept graph is obtained from a word graph through a partial parsing process using semantic grammars. This approach allows us to use relatively complex and better language models along with acoustic models to compute word posterior probabilities. The language model used is comprised of stochastic context free grammars (one for each concept) and an n-gram concept language model. We show that the posterior probabilities computed on concept graphs outperform those computed on word graphs when used as Confidence Measures. Results are presented within the context of Colorado University (CU) Communicator System; a telephone-based dialog system for making travel plans by accessing information about flights, hotels and car rentals.

Erhan Mengusoglu - One of the best experts on this subject based on the ideXlab platform.

  • use of acoustic prior information for Confidence Measure in asr applications
    Conference of the International Speech Communication Association, 2001
    Co-Authors: Erhan Mengusoglu
    Abstract:

    In this paper, we propose a new acoustic Confidence Measure of ASR hypothesis and compare it to approaches proposed in the literature. This approach takes into account prior information on the acoustic model performance specific to each phoneme. The new method is tested on two types of recognition errors: the out-of-vocabulary words and the errors due to additive noise. We then propose an efficient way to interpret the raw Confidence Measure as a correctness prior probability.

  • INTERSPEECH - Use of Acoustic Prior Information for Confidence Measure in ASR Applications
    2001
    Co-Authors: Erhan Mengusoglu
    Abstract:

    In this paper, we propose a new acoustic Confidence Measure of ASR hypothesis and compare it to approaches proposed in the literature. This approach takes into account prior information on the acoustic model performance specific to each phoneme. The new method is tested on two types of recognition errors: the out-of-vocabulary words and the errors due to additive noise. We then propose an efficient way to interpret the raw Confidence Measure as a correctness prior probability.

Gregory Senay - One of the best experts on this subject based on the ideXlab platform.

  • INTERSPEECH - Confidence Measure for speech indexing based on Latent Dirichlet Allocation
    2012
    Co-Authors: Gregory Senay, Georges Linares
    Abstract:

    This paper presents a Confidence Measure for speech indexing that aims to predict the indexing quality of a speech document for a Spoken Document Retrieval (SDR) task. We first introduce how the indexing quality of a speech document is evaluated. Then, we present our method to predict the indexing quality of a speech document. It is based on Confidence Measure provided by an automatic speech recognition system and the detection of semantic outliers implemented with the Latent Dirichlet Allocation (LDA) model. Experiments are conducted on the French Broadcast news campaign ESTER2 in a classical SDR scenario where users submit text-queries to a search engine. Results demonstrate an overall improvement when the detection is done with the LDA model. The detection rate is always above 70%. Index Terms: speech indexing, Confidence Measure, spoken document retrieval, latent dirichlet allocation

  • a segment level Confidence Measure for spoken document retrieval
    International Conference on Acoustics Speech and Signal Processing, 2011
    Co-Authors: Gregory Senay, Georges Linares, Benjamin Lecouteux
    Abstract:

    This paper presents a semantic Confidence Measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition (ASR) Confidence Measure and a Semantic Compacity Index (SCI), that estimates the relevance of the words considering the semantic context in which they occurred. Experiments are conducted on the French Broadcast news corpus ESTER, by simulating a classical SDR usage scenario: users submit text-queries to a search engine that is expected to return the most relevant documents regarding the query. Results demonstrate the interest of using semantic level information to predict the transcription indexability.

  • ICASSP - A segment-level Confidence Measure for Spoken Document Retrieval
    2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011
    Co-Authors: Gregory Senay, Georges Linares, Benjamin Lecouteux
    Abstract:

    This paper presents a semantic Confidence Measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicting method relies on the combination of Automatic Speech Recognition (ASR) Confidence Measure and a Semantic Compacity Index (SCI), that estimates the relevance of the words considering the semantic context in which they occurred. Experiments are conducted on the French Broadcast news corpus ESTER, by simulating a classical SDR usage scenario: users submit text-queries to a search engine that is expected to return the most relevant documents regarding the query. Results demonstrate the interest of using semantic level information to predict the transcription indexability.