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1
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203318 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia (2021)
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2
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
In: https://hal.inria.fr/hal-03203318 ; 2021 (2021)
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3
Graphs, Computation, and Language ...
Ustalov, Dmitry. - : Zenodo, 2021
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4
Graphs, Computation, and Language ...
Ustalov, Dmitry. - : Zenodo, 2021
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5
Adaptation au locuteur pour la séparation de la parole par NMF
In: https://hal.sorbonne-universite.fr/hal-01482183 ; [Stage] STMS - Sciences et Technologies de la Musique et du Son UMR 9912 IRCAM-CNRS-UPMC. 2016 (2016)
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6
On the Generalization of Shannon Entropy for Speech Recognition
In: IEEE workshop on Spoken Language Technology ; https://hal.sorbonne-universite.fr/hal-00737653 ; IEEE workshop on Spoken Language Technology, Dec 2012, United States (2012)
Abstract: International audience ; This paper introduces an entropy-based spectral representation as a measure of the degree of noisiness in audio signals, complementary to the standard MFCCs for audio and speech recognition. The proposed representation is based on the Rényi entropy, which is a generalization of the Shannon entropy. In audio signal representation, Rényi entropy presents the advantage of focusing either on the harmonic content (prominent amplitude within a distribution) or on the noise content (equal distribution of amplitudes). The proposed representation outperforms all other noisiness measures - including Shannon and Wiener entropies - in a large-scale classification of vocal effort (whispered-soft/normal/loud-shouted) in the real scenario of multi-language massive role-playing video games. The improvement is around 10% in relative error reduction, and is particularly significant for the recognition of noisy speech - i.e., whispery/breathy speech. This confirms the role of noisiness for speech recognition, and will further be extended to the classification of voice quality for the design of an automatic voice casting system in video games.
Keyword: [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [STAT.AP]Statistics [stat]/Applications [stat.AP]; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]; expressive speech; information theory; spectral entropy; speech recognition; video games; voice quality
URL: https://hal.sorbonne-universite.fr/hal-00737653
https://hal.sorbonne-universite.fr/hal-00737653/document
https://hal.sorbonne-universite.fr/hal-00737653/file/SLT12_NO_ML.pdf
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7
Research on Narrowband Communications.
In: DTIC AND NTIS (1982)
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8
Research on Narrowband Communications
In: DTIC AND NTIS (1981)
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9
Research on Narrowband Communications
In: DTIC AND NTIS (1981)
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10
Speech Communication
Stevens, Kenneth N.; Halle, Morris; Benhaim, N.. - : Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT), 1966
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11
Tutor Dialogue Planning with Contextual Information and Discourse Structure
In: http://www.cs.cmu.edu/afs/cs/user/rwfisher/www/Curriculum_Vitae_files/fisher_simmons_its14.pdf
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