Cargando…

Extended liquid state machines for speech recognition

A liquid state machine (LSM) is a biologically plausible model of a cortical microcircuit. It exists of a random, sparse reservoir of recurrently connected spiking neurons with fixed synapses and a trainable readout layer. The LSM exhibits low training complexity and enables backpropagation-free lea...

Descripción completa

Detalles Bibliográficos
Autores principales:	Deckers, Lucas, Tsang, Ing Jyh, Van Leekwijck, Werner, Latré, Steven
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2022
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651956/ https://www.ncbi.nlm.nih.gov/pubmed/36389242 http://dx.doi.org/10.3389/fnins.2022.1023470

Ejemplares similares

Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition
por: Soures, Nicholas, et al.
Publicado: (2019)

SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition
por: Srinivasan, Gopalakrishnan, et al.
Publicado: (2018)

Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines
por: Wijesinghe, Parami, et al.
Publicado: (2019)

Multisensory benefits for speech recognition in noisy environments
por: Oh, Yonghee, et al.
Publicado: (2022)

Reinforcement Learning With Low-Complexity Liquid State Machines
por: Ponghiran, Wachirawit, et al.
Publicado: (2019)

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem
por: Wingfield, Cai, et al.
Publicado: (2017)

Multimodal transformer augmented fusion for speech emotion recognition
por: Wang, Yuanyuan, et al.
Publicado: (2023)

A surrogate gradient spiking baseline for speech command recognition
por: Bittar, Alexandre, et al.
Publicado: (2022)

On the similarities of representations in artificial and brain neural networks for speech recognition
por: Wingfield, Cai, et al.
Publicado: (2022)

Speech Recognition via fNIRS Based Brain Signals
por: Liu, Yichuan, et al.
Publicado: (2018)

Liquid State Machine on SpiNNaker for Spatio-Temporal Classification Tasks
por: Patiño-Saucedo, Alberto, et al.
Publicado: (2022)

Extended High Frequencies Provide Both Spectral and Temporal Information to Improve Speech-in-Speech Recognition
por: Trine, Allison, et al.
Publicado: (2020)

Invariant object recognition based on extended fragments
por: Bart, Evgeniy, et al.
Publicado: (2012)

Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants
por: Fontan, Lionel, et al.
Publicado: (2022)

Audiovisual cues benefit recognition of accented speech in noise but not perceptual adaptation
por: Banks, Briony, et al.
Publicado: (2015)

Automatic Speech Recognition from Neural Signals: A Focused Review
por: Herff, Christian, et al.
Publicado: (2016)

Effects of Phase-Locking Deficits on Speech Recognition in Older Adults With Presbycusis
por: Hao, Wenyang, et al.
Publicado: (2018)

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
por: Wu, Jibin, et al.
Publicado: (2020)

Deep Cross-Corpus Speech Emotion Recognition: Recent Advances and Perspectives
por: Zhang, Shiqing, et al.
Publicado: (2021)

Speech Production as State Feedback Control
por: Houde, John F., et al.
Publicado: (2011)

Speech Recognition and Listening Effort in Cochlear Implant Recipients and Normal-Hearing Listeners
por: Abdel-Latif, Khaled H. A., et al.
Publicado: (2022)

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway
por: Schilling, Achim, et al.
Publicado: (2022)

Progressive distribution adapted neural networks for cross-corpus speech emotion recognition
por: Zong, Yuan, et al.
Publicado: (2022)

Changes in Neuronal Representations of Consonants in the Ascending Auditory System and Their Role in Speech Recognition
por: Steadman, Mark A., et al.
Publicado: (2018)

Modulation of tonotopic ventral medial geniculate body is behaviorally relevant for speech recognition
por: Mihai, Paul Glad, et al.
Publicado: (2019)

Pupillometry Assessment of Speech Recognition and Listening Experience in Adult Cochlear Implant Patients
por: Russo, Francesca Yoshie, et al.
Publicado: (2020)

Depression Speech Recognition With a Three-Dimensional Convolutional Network
por: Wang, Hongbo, et al.
Publicado: (2021)

A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition
por: Acharya, Jyotibdha, et al.
Publicado: (2018)

Outer Hair Cell and Auditory Nerve Function in Speech Recognition in Quiet and in Background Noise
por: Hoben, Richard, et al.
Publicado: (2017)

Absence of Rhythm Benefit on Speech in Noise Recognition in Children Diagnosed With Auditory Processing Disorder
por: Sidiras, Christos, et al.
Publicado: (2020)

Effects of Wireless Remote Microphone on Speech Recognition in Noise for Hearing Aid Users in China
por: Chen, Jing, et al.
Publicado: (2021)

Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning
por: Liu, Dong, et al.
Publicado: (2021)

The recognition of time-compressed speech as a function of age in listeners with cochlear implants or normal hearing
por: Tinnemore, Anna R., et al.
Publicado: (2022)

Factors Underlying Individual Differences in Speech-Recognition Threshold (SRT) in Noise Among Older Adults
por: Humes, Larry E.
Publicado: (2021)

Human EEG and Recurrent Neural Networks Exhibit Common Temporal Dynamics During Speech Recognition
por: Hashemnia, Saeedeh, et al.
Publicado: (2021)

Factors influencing the relationship between cochlear health measures and speech recognition in cochlear implant users
por: Zamaninezhad, Ladan, et al.
Publicado: (2023)

OPRA-RS: A Hearing-Aid Fitting Method Based on Automatic Speech Recognition and Random Search
por: Gonçalves Braz, Libio, et al.
Publicado: (2022)

Auditory, Cognitive, and Linguistic Factors Predict Speech Recognition in Adverse Listening Conditions for Children With Hearing Loss
por: McCreery, Ryan W., et al.
Publicado: (2019)

GCF(2)-Net: global-aware cross-modal feature fusion network for speech emotion recognition
por: Li, Feng, et al.
Publicado: (2023)

Band importance for speech-in-speech recognition
por: Buss, Emily, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_qvfpaj0bmocbit9e5g8kiold42