US 12,142,263 B2
Self-learning neuromorphic acoustic model for speech recognition
Lavinia Andreea Danielescu, San Francisco, CA (US); Timothy M. Shea, Merced, CA (US); Kenneth Michael Stewart, Irvine, CA (US); Noah Gideon Pacik-Nelson, Boston, MA (US); and Eric Michael Gallo, Moretown, VT (US)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Sep. 16, 2022, as Appl. No. 17/946,523.
Prior Publication US 2024/0096313 A1, Mar. 21, 2024
Int. Cl. G10L 15/16 (2006.01); G10L 15/06 (2013.01); G10L 15/197 (2013.01); G10L 15/22 (2006.01); G10L 15/30 (2013.01); G10L 25/21 (2013.01)
CPC G10L 15/16 (2013.01) [G10L 15/063 (2013.01); G10L 15/197 (2013.01); G10L 15/22 (2013.01); G10L 15/30 (2013.01); G10L 25/21 (2013.01); G10L 2015/0635 (2013.01); G10L 2015/223 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for recognizing speech, the method comprising:
receiving, a trained acoustic model implemented as a spiking neural network (SNN) on a neuromorphic processor of a client device, a set of feature coefficients that represent acoustic energy of input audio received from a microphone communicably coupled to the client device, wherein the acoustic model is trained to predict speech sounds based on input feature coefficients;
generating, by the acoustic model, output data indicating predicted speech sounds corresponding to the set of feature coefficients that represent the input audio received from the microphone;
updating, by the neuromorphic processor, one or more parameters of the acoustic model using one or more learning rules and the predicted speech sounds of the output data, wherein at least one learning rule is configured to update parameters of the acoustic model based on each speech recognition event for which the acoustic model generates a prediction of speech sounds; and
initiating an action based on the output data.