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Integrating a Phrase Structure Corpus Grammar and a Lexical-Semantic Network: the HOLINET Knowledge Graph
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In: Proceedings of LREC 2022 ; https://hal-amu.archives-ouvertes.fr/hal-03655636 ; Proceedings of LREC 2022, Jun 2022, Marseille, France (2022)
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Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach
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Sequential and network analyses to describe multiple signal use in captive mangabeys
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In: ISSN: 0003-3472 ; EISSN: 1095-8282 ; Animal Behaviour ; https://hal.archives-ouvertes.fr/hal-03480471 ; Animal Behaviour, Elsevier Masson, 2021, 182, pp.203-226. ⟨10.1016/j.anbehav.2021.09.005⟩ (2021)
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Linking an Abstract Corpus Grammar to a Lexical Semantic Network
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In: https://hal.archives-ouvertes.fr/hal-03552630 ; [Research Report] Laboratoire Parole et Langage – Université d’Aix-Marseille. 2021 (2021)
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Coupler syntaxe et sémantique dans une même base de connaissances linguistiques
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In: https://hal.archives-ouvertes.fr/hal-03552622 ; [Rapport de recherche] Laboratoire Parole et Langage – Université d’Aix-Marseille. 2021 (2021)
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Database of word-level statistics for Mandarin Chinese (DoWLS-MAN)
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-03328510 ; Behavior Research Methods, Psychonomic Society, Inc, In press, ⟨10.3758/s13428-021-01620-7⟩ (2021)
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What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
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In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
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Abstract:
The generation of speech, and more generally complex animal vocalizations, by artificial systems is a difficult problem which has recently been addressed using various techniques in artificial intelligence. Generative Adversarial Networks (GANs) have shown very good abilities for generating images, and more recently sounds. The usability of a GAN generating a vocal repertoire relies in part on our understanding of the representations of the various sounds in the GAN latent space. Here, we aim to test the ability of WaveGAN to produce a set of canary syllables and constrain the latent space to a small dimension. We trained WaveGANs with varying latent space dimensions (from 1 to 6) on a large dataset of canary syllables (16000 renditions of 16 different syllable types). The sounds produced by the generators are identified and evaluated by a RNN-based classifier trained on the same dataset. This quantitative evaluation is paired with a qualitative evaluation of the GAN output spectrograms across GAN training epochs and latent dimensions, comparing multiple instances of the training for each condition. Altogether, our results show that a latent space of dimension 3 is enough to produce a varied repertoire of sounds of quality often indistinguishable from real canary ones, spanning all the types of syllables of the dataset. Importantly, we show that the 3-dimensional GAN generalizes by interpolating between the various syllable types. We rely on UMAP representations to qualitatively show the similarities between the training data and the generated data, and between the generated syllables and the interpolations produced. Exploring the latent representations of syllable types, we show that they form well identifiable subspaces of the latent space. This study provides tools to train simple sensorimotor models, as inverse models, from perceived sounds to motor representations of the same sounds. Both the RNN-based classifier and the small dimensional GAN provide a way to learn the mappings of perceived and produced sounds.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; Birdsong; Generative adversarial network; Latent space; Sound generation
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URL: https://hal.inria.fr/hal-03244723/file/Pagliarini2021_canary_GAN__HAL-v1.pdf
https://hal.inria.fr/hal-03244723/document
https://hal.inria.fr/hal-03244723
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| 9 |
Modeling the neural network responsible for song learning ; Modélisation du réseau neuronal responsable de l'apprentissage du chant chez l'oiseau chanteur
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In: https://tel.archives-ouvertes.fr/tel-03217834 ; Modeling and Simulation. Université de Bordeaux, 2021. English. ⟨NNT : 2021BORD0107⟩ (2021)
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Cortical basis of vocalization in behaving freely moving minipigs ; Bases corticales de la vocalisation chez le miniporc en comportement
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In: https://tel.archives-ouvertes.fr/tel-03353386 ; Neuroscience. Université Grenoble Alpes [2020-.], 2021. English. ⟨NNT : 2021GRALS013⟩ (2021)
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| 11 |
Automatic Classification of Phonation Types in Spontaneous Speech: Towards a New Workflow for the Characterization of Speakers’ Voice Quality
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In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03334492 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.1015-1018, ⟨10.21437/Interspeech.2021-1765⟩ (2021)
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Resting functional connectivity in the semantic appraisal network predicts accuracy of emotion identification.
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Development of thalamus mediates paternal age effect on offspring reading: A preliminary investigation.
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In: Human brain mapping, vol 42, iss 14 (2021)
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Dataset of coronavirus content from Instagram with an exploratory analysis
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In: ISSN: 2169-3536 ; EISSN: 2169-3536 ; IEEE Access ; https://hal.archives-ouvertes.fr/hal-03559489 ; IEEE Access, IEEE, 2021, 9, pp.157192-157202. ⟨10.1109/ACCESS.2021.3126552⟩ (2021)
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Phoneme-to-Audio Alignment with Recurrent Neural Networks for Speaking and Singing Voice
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In: Proceedings of Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03552964 ; Proceedings of Interspeech 2021, International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.61-65, ⟨10.21437/interspeech.2021-1676⟩ ; https://www.interspeech2021.org/ (2021)
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Improving Machine Translation of Arabic Dialects through Multi-Task Learning
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In: 20th International Conference Italian Association for Artificial Intelligence:AIxIA 2021 ; https://hal.archives-ouvertes.fr/hal-03435996 ; 20th International Conference Italian Association for Artificial Intelligence:AIxIA 2021, Dec 2021, MILAN/Virtual, Italy (2021)
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| 17 |
Document Domain Randomization for Deep Learning Document Layout Extraction
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In: Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland) ; https://hal.inria.fr/hal-03336444 ; Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland), Sep 2021, Lausanne, Switzerland. pp.497-513, ⟨10.1007/978-3-030-86549-8_32⟩ (2021)
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End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
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In: ISBN: 978-1-6654-0019-0 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) ; https://hal.archives-ouvertes.fr/hal-03405970 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Sep 2021, Nara, Japan ; https://www.acii-conf.net/2021/ (2021)
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