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pro vyhledávání: '"Attia, Ahmed Adel"'
Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students. In this work, we study the efficacy of continued pretraining (CPT) in
Externí odkaz:
http://arxiv.org/abs/2409.14494
Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students. In this work, we study the efficacy of continued pretraining (CPT) in
Externí odkaz:
http://arxiv.org/abs/2405.13018
The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs. Better input and output representation has the potential to boost models' performance and ge
Externí odkaz:
http://arxiv.org/abs/2309.09220
Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data. However, this progress doesn't readily extend to AS
Externí odkaz:
http://arxiv.org/abs/2309.07927
Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability of pellet placements, and existing methods for mapping anatomical lan
Externí odkaz:
http://arxiv.org/abs/2305.10775
Autor:
Attia, Ahmed Adel, Espy-Wilson, Carol
Articulatory recordings track the positions and motion of different articulators along the vocal tract and are widely used to study speech production and to develop speech technologies such as articulatory based speech synthesizers and speech inversi
Externí odkaz:
http://arxiv.org/abs/2210.15195
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion system, we
Externí odkaz:
http://arxiv.org/abs/2205.13086
Autor:
Attia, Ahmed Adel, Espy-Wilson, Carol
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Articulatory recordings track the positions and motion of different articulators along the vocal tract and are widely used to study speech production and to develop speech technologies such as articulatory based speech synthesizers and speech inversi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dcc382ac5257d723c8143ac71008ecb
https://doi.org/10.1109/icassp49357.2023.10096209
https://doi.org/10.1109/icassp49357.2023.10096209
