公开(公告)号：US20230015189A1

公开(公告)日：2023-01-19

申请号：US17953156

申请日：2022-09-26

Applicant: Pindrop Security, Inc.

Inventor： David Looney ,  Nikolay D. Gaubitch

IPC: G10L25/51 ,  G10L25/90 ,  G10L15/06 ,  G10L15/22

Abstract: A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.

公开(公告)号：US11495244B2

公开(公告)日：2022-11-08

申请号：US16375785

申请日：2019-04-04

Applicant: PINDROP SECURITY, INC.

Inventor： David Looney ,  Nikolay D. Gaubitch

IPC: G10L25/00 ,  G10L25/51 ,  G10L25/90 ,  G10L15/06 ,  G10L15/22 ,  H04M3/436

Abstract: A computer may train a single-class machine learning using normal speech recordings. The machine learning model or any other model may estimate the normal range of parameters of a physical speech production model based on the normal speech recordings. For example, the computer may use a source-filter model of speech production, where voiced speech is represented by a pulse train and unvoiced speech by a random noise and a combination of the pulse train and the random noise is passed through an auto-regressive filter that emulates the human vocal tract. The computer leverages the fact that intentional modification of human voice introduces errors to source-filter model or any other physical model of speech production. The computer may identify anomalies in the physical model to generate a voice modification score for an audio signal. The voice modification score may indicate a degree of abnormality of human voice in the audio signal.