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21
Transfer learning of language-independent end-to-end ASR with language model fusion ...
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22
Multi-Head Decoder for End-to-End Speech Recognition ...
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23
Low-Resource Contextual Topic Identification on Speech ...
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24
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling ...
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25
An analysis of environment, microphone and data simulation mismatches in robust speech recognition
In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.inria.fr/hal-01399180 ; Computer Speech and Language, Elsevier, 2017, 46, pp.535-557. ⟨10.1016/j.csl.2016.11.005⟩ (2017)
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26
The third 'CHIME' speech separation and recognition challenge: Analysis and outcomes
In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.inria.fr/hal-01382108 ; Computer Speech and Language, Elsevier, 2017, 46, pp.605-626. ⟨10.1016/j.csl.2016.10.005⟩ (2017)
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27
CHiME2 Grid
Vincent, Emmanuel; Barker, Jon; Watanabe, Shinji. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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28
CHiME3
Barker, Jon; Marxer, Ricard; Vincent, Emmanuel. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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29
CHiME2 WSJ0
Vincent, Emmanuel; Barker, Jon; Watanabe, Shinji. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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30
CHiME2 WSJ0 ...
Vincent, Emmanuel; Barker, Jon; Watanabe, Shinji. - : Linguistic Data Consortium, 2017
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31
CHiME2 Grid ...
Vincent, Emmanuel; Barker, Jon; Watanabe, Shinji. - : Linguistic Data Consortium, 2017
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32
CHiME3 ...
Barker, Jon; Marxer, Ricard; Vincent, Emmanuel; Watanabe, Shinji. - : Linguistic Data Consortium, 2017
Abstract:

Introduction

CHiME3 was developed as part of The 3rd CHiME Speech Separation and Recognition Challenge and contains approximately 342 hours of English speech and transcripts from noisy environments and 50 hours of noisy environment audio. The CHiME Challenges focus on distant-microphone automatic speech recognition (ASR) in real-world environments. See the CHIME3 home page for more information. The task in CHiME3 was similar to the medium vocabulary track of the CHiME2 Challenge in that the target utterances were taken from CSR-I (WSJ0) Complete (LDC93S6A), specifically, the 5,000 word subset of read speech from Wall Street Journal news text. CHiME3 involved two types of ...
URL: https://catalog.ldc.upenn.edu/LDC2017S24
https://dx.doi.org/10.35111/v154-hj21
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33
Bayesian speech and language processing
Watanabe, Shinji; Chien, Jen-tzung. - Cambridge : Cambridge University Press, 2015
BLLDB
UB Frankfurt Linguistik
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34
The third `CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
In: 2015 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015) ; https://hal.inria.fr/hal-01211376 ; 2015 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2015), Dec 2015, Scottsdale, AZ, United States (2015)
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35
Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial, spectral and temporal modeling of sounds
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 27 (2013) 3, 851-873
OLC Linguistik
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36
Cluster-based dynamic variance adaptation for interconnecting speech enhancement pre-processor and speech recognizer
In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 27 (2013) 1, 350-368
OLC Linguistik
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37
Influence relation estimation based on lexical entrainment in conversation
In: Speech communication. - Amsterdam [u.a.] : Elsevier 55 (2013) 2, 329-339
OLC Linguistik
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38
Prior-shared feature and model space speaker adaptation by consistently employing map estimation
In: Speech communication. - Amsterdam [u.a.] : Elsevier 55 (2013) 3, 415-431
OLC Linguistik
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39
Low-latency real-time meeting recognition and understanding using distant microphones and omni-directional camera
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 20 (2012) 2, 499-513
BLLDB
OLC Linguistik
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40
Frame-wise model re-estimation method based on Gaussian pruning with weight normalization for noise robust voice activity detection
In: Speech communication. - Amsterdam [u.a.] : Elsevier 54 (2012) 2, 229-244
BLLDB
OLC Linguistik
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