US 10,891,949 B2
Vehicle language processing
Praveen Narayanan, San Jose, CA (US); Lisa Scaria, Milpitas, CA (US); Ryan Burke, Palo Alto, CA (US); Francois Charette, Tracy, CA (US); Punarjay Chakravarty, Mountain View, CA (US); and Kaushik Balakrishnan, Mountain View, CA (US)
Assigned to FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US)
Filed by Ford Global Technologies, LLC, Dearborn, MI (US)
Filed on Sep. 10, 2018, as Appl. No. 16/125,944.
Prior Publication US 2020/0082817 A1, Mar. 12, 2020
Int. Cl. G10L 21/00 (2013.01); G10L 15/22 (2006.01); G10L 15/16 (2006.01); G10L 25/21 (2013.01); G10L 15/30 (2013.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); B60W 10/04 (2006.01); B60W 10/20 (2006.01); B60W 10/18 (2012.01); B60W 50/10 (2012.01); B60W 50/00 (2006.01)
CPC G10L 15/22 (2013.01) [B60W 10/04 (2013.01); B60W 10/18 (2013.01); B60W 10/20 (2013.01); B60W 50/10 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G10L 15/16 (2013.01); G10L 15/30 (2013.01); G10L 25/21 (2013.01); B60W 2050/007 (2013.01); B60W 2540/21 (2020.02); G10L 2015/223 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving a spoken language command in response to emitting a spoken language cue;
training a neural network (NN) at least in part by inputting mel-frequency data from the spoken language command combined with a loss function to a convolutional filter bank that outputs modified mel-frequency data to a bidirectional long short-term memory that in turn outputs audio spectrum data to determine a vehicle command using a generalized adversarial neural network (GAN) that determines a loss function by a combination of a binary classification of the output audio spectrum data as one of real or fake and a ground truth-based loss;
processing the spoken language command with the NN to determine the vehicle command; and
operating a vehicle based on the vehicle command.