LANGUAGE MODEL BIASING MODULATION

• US 20160379625A1
• Filed: 09/13/2016
• Published: 12/29/2016
• Est. Priority Date: 03/30/2015
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Moreno-Mengibar, Pedro J., Aleksic, Petar
• Granted Patent: US 9,886,946 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G10L 15/07 to the speaker
• CPC Class Codes: G10L 15/183 using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 15/24 Speech recognition using non-acoustical features

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating language model biasing. In some implementations, context data is received. A likely context associated with a user is determined based on at least a portion of the context data. One or more language model biasing parameters based at least on the likely context associated with the user is selected. A context confidence score associated with the likely context based on at least a portion of the context data is determined. One or more language model biasing parameters based at least on the context confidence score is adjusted. A baseline language model based at least on the one or more of the adjusted language model biasing parameters is biased. The baseline language model is provided for use by an automated speech recognizer (ASR).

Claims

1. A computer-implemented method comprising:

receiving context data;

determining a likely context associated with a user, based on at least a portion of the context data;

selecting one or more language model biasing parameters based at least on the likely context associated with the user;

determining a context confidence score associated with the likely context based on at least a portion of the context data;

adjusting one or more of the language model biasing parameters based at least on the context confidence score;

biasing a baseline language model based at least on one or more of the adjusted language model biasing parameters; and

providing the biased language model for use by an automated speech recognizer (ASR).