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Speaker Recognition and Characterization (SPE-SPKR)

A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT


End-to-end systems using deep neural networks have been widely studied in the field of speaker verification. Raw audio signal processing has also been widely studied in the fields of automatic music tagging and speech recognition. However, as far as we know, end-to-end systems using raw audio signals have not been explored in speaker verification. In this paper, a complete end-to-end speaker verification system is proposed, which inputs raw audio signals and outputs the verification results. A pre-processing layer and the embedded speaker feature extraction models were mainly investigated.

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19 April 2018 - 2:14pm
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[1] , "A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2989. Accessed: Nov. 19, 2019.
@article{2989-18,
url = {http://sigport.org/2989},
author = { },
publisher = {IEEE SigPort},
title = {A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT},
year = {2018} }
TY - EJOUR
T1 - A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2989
ER -
. (2018). A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT. IEEE SigPort. http://sigport.org/2989
, 2018. A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT. Available at: http://sigport.org/2989.
. (2018). "A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT." Web.
1. . A COMPLETE END-TO-END SPEAKER VERIFICATION SYSTEM USING DEEP NEURAL NETWORKS: FROM RAW SIGNALS TO VERIFICATION RESULT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2989

END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA

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Authors:
Anna Silnova, Mireia Diez, Oldrich Plchot, Pavel Matejka, Lukas Burget
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18 April 2018 - 12:24pm
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End-to-End_ICASSP_2018.pdf

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[1] Anna Silnova, Mireia Diez, Oldrich Plchot, Pavel Matejka, Lukas Burget, "END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2967. Accessed: Nov. 19, 2019.
@article{2967-18,
url = {http://sigport.org/2967},
author = {Anna Silnova; Mireia Diez; Oldrich Plchot; Pavel Matejka; Lukas Burget },
publisher = {IEEE SigPort},
title = {END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA},
year = {2018} }
TY - EJOUR
T1 - END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA
AU - Anna Silnova; Mireia Diez; Oldrich Plchot; Pavel Matejka; Lukas Burget
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2967
ER -
Anna Silnova, Mireia Diez, Oldrich Plchot, Pavel Matejka, Lukas Burget. (2018). END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA. IEEE SigPort. http://sigport.org/2967
Anna Silnova, Mireia Diez, Oldrich Plchot, Pavel Matejka, Lukas Burget, 2018. END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA. Available at: http://sigport.org/2967.
Anna Silnova, Mireia Diez, Oldrich Plchot, Pavel Matejka, Lukas Burget. (2018). "END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA." Web.
1. Anna Silnova, Mireia Diez, Oldrich Plchot, Pavel Matejka, Lukas Burget. END-TO-END DNN BASED SPEAKER RECOGNITION INSPIRED BY I-VECTOR AND PLDA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2967

Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification


Phonetic variability is one of the primary challenges in short duration speaker verification. This paper proposes a novel method that modifies the standard normal distribution prior in the total variability model to use a mixture of Gaussians as the prior distribution. The proposed speaker-phonetic vectors are then estimated from the posterior probability of latent variables, and each vector has a phonetic meaning.

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Authors:
Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee
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18 April 2018 - 3:07am
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[1] Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee, "Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2960. Accessed: Nov. 19, 2019.
@article{2960-18,
url = {http://sigport.org/2960},
author = {Jianbo Ma; Vidhyasaharan Sethu; Eliathamby Ambikairajah; Kong Aik Lee },
publisher = {IEEE SigPort},
title = {Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification},
year = {2018} }
TY - EJOUR
T1 - Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification
AU - Jianbo Ma; Vidhyasaharan Sethu; Eliathamby Ambikairajah; Kong Aik Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2960
ER -
Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee. (2018). Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification. IEEE SigPort. http://sigport.org/2960
Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee, 2018. Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification. Available at: http://sigport.org/2960.
Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee. (2018). "Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification." Web.
1. Jianbo Ma, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Kong Aik Lee. Speaker-Phonetic Vector Estimation for Short Duration Speaker Verification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2960

GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION


In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection.

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Authors:
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno
Submitted On:
18 April 2018 - 11:00am
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ICASSP 2018 GE2E.pptx

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[1] Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno, "GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2778. Accessed: Nov. 19, 2019.
@article{2778-18,
url = {http://sigport.org/2778},
author = {Li Wan; Quan Wang; Alan Papir; Ignacio Lopez Moreno },
publisher = {IEEE SigPort},
title = {GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION},
year = {2018} }
TY - EJOUR
T1 - GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION
AU - Li Wan; Quan Wang; Alan Papir; Ignacio Lopez Moreno
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2778
ER -
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno. (2018). GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION. IEEE SigPort. http://sigport.org/2778
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno, 2018. GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION. Available at: http://sigport.org/2778.
Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno. (2018). "GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION." Web.
1. Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno. GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2778

A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification


A novel learnable dictionary encoding layer is proposed in this paper for end-to-end language identification. It is inline with the conventional GMM i-vector approach both theoretically and practically. We imitate the mechanism of traditional GMM training and Supervector encoding procedure on the top of CNN. The proposed layer can accumulate high-order statistics from variable-length input sequence and generate an utterance level fixed-dimensional vector representation.

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Authors:
Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li
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13 April 2018 - 9:37am
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[1] Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li, "A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2701. Accessed: Nov. 19, 2019.
@article{2701-18,
url = {http://sigport.org/2701},
author = {Weicheng Cai; Zexin Cai; Xiang Zhang; Xiaoqi Wang; Ming Li },
publisher = {IEEE SigPort},
title = {A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification},
year = {2018} }
TY - EJOUR
T1 - A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification
AU - Weicheng Cai; Zexin Cai; Xiang Zhang; Xiaoqi Wang; Ming Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2701
ER -
Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li. (2018). A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification. IEEE SigPort. http://sigport.org/2701
Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li, 2018. A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification. Available at: http://sigport.org/2701.
Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li. (2018). "A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification." Web.
1. Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li. A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2701

Insights into End-to-End Learning Scheme for Language Identification


A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically. In the end-to-end pipeline, a general encoding layer is employed on top of the front-end CNN, so that it can encode the variable-length input sequence into an utterance level vector automatically. After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline.

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Authors:
Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li
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13 April 2018 - 9:32am
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[1] Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li, "Insights into End-to-End Learning Scheme for Language Identification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2699. Accessed: Nov. 19, 2019.
@article{2699-18,
url = {http://sigport.org/2699},
author = {Weicheng Cai; Zexin Cai; Wenbo Liu; Xiaoqi Wang; Ming Li },
publisher = {IEEE SigPort},
title = {Insights into End-to-End Learning Scheme for Language Identification},
year = {2018} }
TY - EJOUR
T1 - Insights into End-to-End Learning Scheme for Language Identification
AU - Weicheng Cai; Zexin Cai; Wenbo Liu; Xiaoqi Wang; Ming Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2699
ER -
Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li. (2018). Insights into End-to-End Learning Scheme for Language Identification. IEEE SigPort. http://sigport.org/2699
Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li, 2018. Insights into End-to-End Learning Scheme for Language Identification. Available at: http://sigport.org/2699.
Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li. (2018). "Insights into End-to-End Learning Scheme for Language Identification." Web.
1. Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li. Insights into End-to-End Learning Scheme for Language Identification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2699

DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION


This paper deals with far-field speaker recognition. On a corpus of NIST SRE 2010 data retransmitted in a real room with multiple microphones, we first demonstrate how room acoustics cause significant degradation of state-of-the-art i-vector based speaker recognition system. We then investigate several techniques to improve the performances ranging from probabilistic linear discriminant analysis (PLDA) re-training, through dereverberation, to beamforming.

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Authors:
Ladislav Mosner, Pavel Matejka, Ondrej Novotny, Jan Cernocky
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13 April 2018 - 5:47am
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[1] Ladislav Mosner, Pavel Matejka, Ondrej Novotny, Jan Cernocky, "DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2672. Accessed: Nov. 19, 2019.
@article{2672-18,
url = {http://sigport.org/2672},
author = {Ladislav Mosner; Pavel Matejka; Ondrej Novotny; Jan Cernocky },
publisher = {IEEE SigPort},
title = {DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION
AU - Ladislav Mosner; Pavel Matejka; Ondrej Novotny; Jan Cernocky
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2672
ER -
Ladislav Mosner, Pavel Matejka, Ondrej Novotny, Jan Cernocky. (2018). DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION. IEEE SigPort. http://sigport.org/2672
Ladislav Mosner, Pavel Matejka, Ondrej Novotny, Jan Cernocky, 2018. DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION. Available at: http://sigport.org/2672.
Ladislav Mosner, Pavel Matejka, Ondrej Novotny, Jan Cernocky. (2018). "DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION." Web.
1. Ladislav Mosner, Pavel Matejka, Ondrej Novotny, Jan Cernocky. DEREVERBERATION AND BEAMFORMING IN FAR-FIELD SPEAKER RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2672

END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM


Recently, hierarchical language identification systems have shown significant improvement over single level systems in both closed and open set language identification tasks. However, developing such a system requires the features and classifier selection at each node in the hierarchical structure to be hand crafted. Motivated by the superior ability of end-to-end deep neural network architecture to jointly optimize the feature extraction and classification process, we propose a novel approach developing an end-to-end hierarchical language identification system.

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Authors:
Saad Irtza, Vidhasaharan Sethu, Eliathamby Ambikairajah, Haizhou Li
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12 April 2018 - 9:20pm
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Poster

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[1] Saad Irtza, Vidhasaharan Sethu, Eliathamby Ambikairajah, Haizhou Li, "END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2538. Accessed: Nov. 19, 2019.
@article{2538-18,
url = {http://sigport.org/2538},
author = {Saad Irtza; Vidhasaharan Sethu; Eliathamby Ambikairajah; Haizhou Li },
publisher = {IEEE SigPort},
title = {END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM},
year = {2018} }
TY - EJOUR
T1 - END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM
AU - Saad Irtza; Vidhasaharan Sethu; Eliathamby Ambikairajah; Haizhou Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2538
ER -
Saad Irtza, Vidhasaharan Sethu, Eliathamby Ambikairajah, Haizhou Li. (2018). END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM. IEEE SigPort. http://sigport.org/2538
Saad Irtza, Vidhasaharan Sethu, Eliathamby Ambikairajah, Haizhou Li, 2018. END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM. Available at: http://sigport.org/2538.
Saad Irtza, Vidhasaharan Sethu, Eliathamby Ambikairajah, Haizhou Li. (2018). "END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM." Web.
1. Saad Irtza, Vidhasaharan Sethu, Eliathamby Ambikairajah, Haizhou Li. END-TO-END HIERARCHICAL LANGUAGE IDENTIFICATION SYSTEM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2538

MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION

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12 April 2018 - 3:24pm
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[1] , "MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2476. Accessed: Nov. 19, 2019.
@article{2476-18,
url = {http://sigport.org/2476},
author = { },
publisher = {IEEE SigPort},
title = {MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION},
year = {2018} }
TY - EJOUR
T1 - MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2476
ER -
. (2018). MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION. IEEE SigPort. http://sigport.org/2476
, 2018. MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION. Available at: http://sigport.org/2476.
. (2018). "MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION." Web.
1. . MULTISTREAM DIARIZATION FUSION USING THE MINIMUM VARIANCE BAYESIAN INFORMATION CRITERION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2476

Making Likelihood Ratios Digestible for Cross-Application Performance Assessment


Performance estimation is crucial to the assessment of novel algorithms and systems. In detection error trade-off (DET) diagrams, discrimination performance is solely assessed targeting one application, where cross-application performance considers risks resulting from decisions, depending on application constraints. For the purpose of interchangeability of research results across different application constraints, we propose to augment DET curves by depicting systems regarding their support of security and convenience levels.

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Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch
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12 April 2018 - 1:11pm
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[1] Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch, "Making Likelihood Ratios Digestible for Cross-Application Performance Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2447. Accessed: Nov. 19, 2019.
@article{2447-18,
url = {http://sigport.org/2447},
author = {Andreas Nautsch; Didier Meuwly; Daniel Ramos; Jonas Lindh; Christoph Busch },
publisher = {IEEE SigPort},
title = {Making Likelihood Ratios Digestible for Cross-Application Performance Assessment},
year = {2018} }
TY - EJOUR
T1 - Making Likelihood Ratios Digestible for Cross-Application Performance Assessment
AU - Andreas Nautsch; Didier Meuwly; Daniel Ramos; Jonas Lindh; Christoph Busch
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2447
ER -
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. (2018). Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. IEEE SigPort. http://sigport.org/2447
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch, 2018. Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. Available at: http://sigport.org/2447.
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. (2018). "Making Likelihood Ratios Digestible for Cross-Application Performance Assessment." Web.
1. Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. Making Likelihood Ratios Digestible for Cross-Application Performance Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2447

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