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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6 7 8...286 Hits 61 – 80 of 5.709 61 LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech Evain, Solène; Nguyen, Ha; Le, Hang... In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021) BASE Show details 62 Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition Liu, Li; Feng, Gang; Beautemps, Denis... In: ISSN: 1520-9210 ; IEEE Transactions on Multimedia ; https://hal.archives-ouvertes.fr/hal-02433830 ; IEEE Transactions on Multimedia, Institute of Electrical and Electronics Engineers, 2021, 23, pp.292-305. ⟨10.1109/TMM.2020.2976493⟩ (2021) BASE Show details 63 Brain-Inspired Audio-Visual Information Processing Using Spiking Neural Networks Wendt, Anne. - : Auckland University of Technology, 2021 BASE Show details 64 Identifying Speaker State from Multimodal Cues Yang, Zixiaofan. - 2021 BASE Show details 65 Unsupervised Morphological Segmentation and Part-of-Speech Tagging for Low-Resource Scenarios Eskander, Ramy. - 2021 BASE Show details 66 Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon Veisi, Hadi; Hosseini, Hawre; Mohammadamini, Mohammad... In: https://hal.archives-ouvertes.fr/hal-03140680 ; 2021 (2021) BASE Show details 67 Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks MACAIRE, Cécile In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021) BASE Show details 68 COSMO-Onset: A Neurally-Inspired Computational Model of Spoken Word Recognition, Combining Top-Down Prediction and Bottom-Up Detection of Syllabic Onsets Nabé, Mamady; Schwartz, Jean-Luc; Diard, Julien In: ISSN: 1662-5137 ; Frontiers in Systems Neuroscience ; https://hal.archives-ouvertes.fr/hal-03318691 ; Frontiers in Systems Neuroscience, Frontiers, 2021, 15, pp.653975. ⟨10.3389/fnsys.2021.653975⟩ (2021) BASE Show details 69 Speech Normalization and Data Augmentation Techniques Based on Acoustical and Physiological Constraints and Their Applications to Child Speech Recognition Yeung, Gary Joseph. - : eScholarship, University of California, 2021 BASE Show details 70 Automatic Speech Recognition : from hybrid to end-to-end approach ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End Heba, Abdelwahab. - : HAL CCSD, 2021 In: https://tel.archives-ouvertes.fr/tel-03616588 ; Intelligence artificielle [cs.AI]. Université Paul Sabatier - Toulouse III, 2021. Français. ⟨NNT : 2021TOU30116⟩ (2021) BASE Show details 71 Large vocabulary automatic speech recognition: from hybrid to end-to-end approaches ; Reconnaissance automatique de la parole à large vocabulaire : des approches hybrides aux approches End-to-End Heba, Abdelwahab. - : HAL CCSD, 2021 In: https://hal.archives-ouvertes.fr/tel-03269807 ; Son [cs.SD]. Université toulouse 3 Paul Sabatier, 2021. Français (2021) BASE Show details 72 Privacy and utility of x-vector based speaker anonymization Srivastava, Brij Mohan Lal; Maouche, Mohamed; Sahidullah, Md... In: https://hal.inria.fr/hal-03197376 ; 2021 (2021) BASE Show details 73 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 74 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 75 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 76 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 77 The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03332224 ; 2021 (2021) BASE Show details 78 Enhancing Speech Privacy with Slicing Maouche, Mohamed; Srivastava, Brij Mohan Lal; Vauquier, Nathalie... In: https://hal.inria.fr/hal-03369137 ; 2021 (2021) BASE Show details 79 Supplementary material to the paper The VoicePrivacy 2020 Challenge: Results and findings Tomashenko, Natalia; Wang, Xin; Vincent, Emmanuel... In: https://hal.archives-ouvertes.fr/hal-03335126 ; 2021 (2021) BASE Show details 80 Training RNN Language Models on Uncertain ASR Hypotheses in Limited Data Scenarios Sheikh, Imran,; Vincent, Emmanuel; Illina, Irina In: https://hal.inria.fr/hal-03327306 ; 2021 (2021) Abstract: Training domain-specific automatic speech recognition (ASR) systems requires a suitable amount of data comprising the target domain. In several scenarios, such as early development stages, privacy-critical applications, or under-resourced languages, only a limited amount of in-domain speech data and an even smaller amount of manual text transcriptions, if any, are available. This motivates the study of ASR language models (LMs) learned from a limited amount of in-domain speech data. Early works have attempted training of n-gram LMs from ASR N-best lists and lattices but training and adaptation of recurrent neural network (RNN) LMs from ASR transcripts has not received attention. In this work, we study training and adaptation of RNN LMs using alternate and uncertain ASR hypotheses embedded in ASR confusion networks obtained from target domain speech data.We explore different methods for training the RNN LMs to deal with the uncertain input sequences. The first method extends the cross-entropy objective into a Kullback–Leibler (KL) divergence based training loss, the second method formulates a training loss based on a hidden Markov model (HMM), and the third method performs training on paths sampled from the confusion networks. These methods are applied to limited data setups including telephone and meeting conversation datasets. Performance is evaluated under two settings wherein no manual transcriptions or a small amount of manual transcriptions are available to aid the training. Moreover, a model adaptation setting is also evaluated wherein the RNN LM is pre-trained on an out-of-domain conversational corpus. Overall the sampling method for training RNN LMs on ASR confusion networks performs the best, and results in up to 12% relative reduction in perplexity on the meeting dataset as compared to training on ASR 1-best hypotheses, without any manual transcriptions. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; automatic speech recognition; confusion networks; language models; recurrent neural networks URL: https://hal.inria.fr/hal-03327306/document https://hal.inria.fr/hal-03327306/file/cn2lm_manuscript.pdf https://hal.inria.fr/hal-03327306 BASE Hide details Page: 1 2 3 4 5 6 7 8...286

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