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Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics ...
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LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading ...
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Neural Network Learning for Robust Speech Recognition
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Qu, Leyuan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2021
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Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives ...
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Conversational Language Learning for Human-Robot Interaction
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| 6 |
Crossmodal Language Grounding in an Embodied Neurocognitive Model
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In: Front Neurorobot (2020)
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Incorporating End-to-End Speech Recognition Models for Sentiment Analysis ...
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Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots ...
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GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection ...
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Syntactic Reanalysis in Language Models for Speech Recognition
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In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) ; https://hal.inria.fr/hal-01558462 ; 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Sep 2017, Lisbon, Portugal ; http://icdl-epirob.org/ (2017)
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Abstract:
International audience ; State-of-the-art speech recognition systems steadily increase their performance using different variants of deep neural networks and postprocess the results by employing N-gram statistical models trained on a large amount of data coming from the general-purpose domain. While achieving an excellent performance regarding Word Error Rate (17.343% on our Human-Robot Interaction data set), state-of-the-art systems generate hypotheses that are grammatically incorrect in 57.316% of the cases. Moreover, if employed in a restricted domain (e.g. Human-Robot Interaction), around 50% of the hypotheses contain out-of-domain words. The latter are confused with similarly pronounced in-domain words and cannot be interpreted by a domain-specific inference system. The state-of-the-art speech recognition systems lack a mechanism that addresses syntactic correctness of hypotheses. We propose a system that can detect and repair grammatically incorrect or infrequent sentence forms. It is inspired by a computational neuroscience model that we developed previously. The current system is still a proof-of-concept version of a future neurobiologically more plausible neural network model. Hence, the resulting system postprocesses sentence hypotheses of state-of-the-art speech recognition systems, producing in-domain words in 100% of the cases, syntactically and grammatically correct hypotheses in 90.319% of the cases. Moreover, it reduces the Word Error Rate to 11.038%.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SCCO.LING]Cognitive science/Linguistics; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; domain-specific; grapheme; Human-Robot Interaction; N-gram; natural language processing; phoneme; speech recognition; syntactic reanalysis; syntax
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URL: https://hal.inria.fr/hal-01558462v2/document
https://hal.inria.fr/hal-01558462v2/file/twiefel_ICDL_EpiRob_2017__generated_by_xav.pdf
https://hal.inria.fr/hal-01558462
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| 11 |
Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture ...
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Recurrent Neural Network for Syntax Learning with Flexible Predicates for Robotic Architectures
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In: The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB) ; https://hal.inria.fr/hal-01417697 ; The Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Sep 2016, Cergy, France ; http://icdl-epirob.org/ (2016)
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Semantic Role Labelling for Robot Instructions using Echo State Networks
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In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) ; https://hal.inria.fr/hal-01417701 ; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2016, Bruges, Belgium ; https://www.elen.ucl.ac.be/esann/index.php?pg=esann16_programme (2016)
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Using Natural Language Feedback in a Neuro-inspired Integrated Multimodal Robotic Architecture
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In: 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) ; https://hal.inria.fr/hal-01417706 ; 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Aug 2016, New York City, United States. pp.52 - 57, ⟨10.1109/ROMAN.2016.7745090⟩ ; http://www.tc.columbia.edu/conferences/roman2016/ (2016)
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Natural language acquisition in recurrent neural architectures ; Erwerb von natürlicher Sprache in rekurrenten neuronalen Architekturen
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Heinrich, Stefan. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2016
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A Recurrent Neural Network for Multiple Language Acquisition: Starting with English and French
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In: Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo 2015) ; https://hal.inria.fr/hal-02561258 ; Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches (CoCo 2015), Dec 2015, Montreal, Canada ; http://ceur-ws.org/Vol-1583/ (2015)
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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives
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