TY - JOUR
T1 - Biologically plausible speech recognition with LSTM neural nets
AU - Graves, Alex
AU - Eck, Douglas
AU - Beringer, Nicole
AU - Schmidhuber, Juergen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2004/1/1
Y1 - 2004/1/1
N2 - Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) are local in space and time and closely related to a biological model of memory in the prefrontal cortex. Not only are they more biologically plausible than previous artificial RNNs, they also outperformed them on many artificially generated sequential processing tasks. This encouraged us to apply LSTM to more realistic problems, such as the recognition of spoken digits. Without any modification of the underlying algorithm, we achieved results comparable to state-of-the-art Hidden Markov Model (HMM) based recognisers on both the TIDIGITS and TI46 speech corpora. We conclude that LSTM should be further investigated as a biologically plausible basis for a bottom-up, neural net-based approach to speech recognition. © Springer-Verlag 2004.
AB - Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) are local in space and time and closely related to a biological model of memory in the prefrontal cortex. Not only are they more biologically plausible than previous artificial RNNs, they also outperformed them on many artificially generated sequential processing tasks. This encouraged us to apply LSTM to more realistic problems, such as the recognition of spoken digits. Without any modification of the underlying algorithm, we achieved results comparable to state-of-the-art Hidden Markov Model (HMM) based recognisers on both the TIDIGITS and TI46 speech corpora. We conclude that LSTM should be further investigated as a biologically plausible basis for a bottom-up, neural net-based approach to speech recognition. © Springer-Verlag 2004.
UR - http://link.springer.com/10.1007/978-3-540-27835-1_10
UR - http://www.scopus.com/inward/record.url?scp=35048892582&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-27835-1_10
DO - 10.1007/978-3-540-27835-1_10
M3 - Article
SN - 1611-3349
VL - 3141
SP - 127
EP - 136
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -