公开(公告)号：US10482876B2

公开(公告)日：2019-11-19

申请号：US16148884

申请日：2018-10-01

Applicant: Interactions LLC

Inventor： Ethan Selfridge ,  Michael Johnston

IPC: G06F17/27 ,  G10L15/197 ,  G10L15/02 ,  G10L15/06

Abstract: A speech interpretation module interprets the audio of user utterances as sequences of words. To do so, the speech interpretation module parameterizes a literal corpus of expressions by identifying portions of the expressions that correspond to known concepts, and generates a parameterized statistical model from the resulting parameterized corpus. When speech is received the speech interpretation module uses a hierarchical speech recognition decoder that uses both the parameterized statistical model and language sub-models that specify how to recognize a sequence of words. The separation of the language sub-models from the statistical model beneficially reduces the size of the literal corpus needed for training, reduces the size of the resulting model, provides more fine-grained interpretation of concepts, and improves computational efficiency by allowing run-time incorporation of the language sub-models.

公开(公告)号：US20170301346A1

公开(公告)日：2017-10-19

申请号：US15131833

申请日：2016-04-18

Applicant: Interactions LLC

Inventor： Ethan Selfridge ,  Michael Johnston

IPC: G10L15/197 ,  G10L15/02 ,  G10L15/06

CPC classification number: G10L15/197 ,  G10L15/02 ,  G10L15/063 ,  G10L2015/0631

Abstract: A speech interpretation module interprets the audio of user utterances as sequences of words. To do so, the speech interpretation module parameterizes a literal corpus of expressions by identifying portions of the expressions that correspond to known concepts, and generates a parameterized statistical model from the resulting parameterized corpus. When speech is received the speech interpretation module uses a hierarchical speech recognition decoder that uses both the parameterized statistical model and language sub-models that specify how to recognize a sequence of words. The separation of the language sub-models from the statistical model beneficially reduces the size of the literal corpus needed for training, reduces the size of the resulting model, provides more fine-grained interpretation of concepts, and improves computational efficiency by allowing run-time incorporation of the language sub-models.