Content-aware speaker recognition

Patent metadata

Field	Value
Publication number	US-9336781-B2
Application number	US-201414264916-A
Country	US
Kind code	B2
Filing date	Apr 29, 2014
Priority date	Oct 17, 2013
Publication date	May 10, 2016
Grant date	May 10, 2016

Abstract

A content-aware speaker recognition system includes technologies to, among other things, analyze phonetic content of a speech sample, incorporate phonetic content of the speech sample into a speaker model, and use the phonetically-aware speaker model for speaker recognition.

First claim

The invention claimed is:

1. A text-independent speaker recognition system comprising:
a front end module embodied in one or more non-transitory computer readable media and executable by at least one computer device to: process an audio signal comprising a current sample of natural language speech; identify a speech segment in the current sample of natural language speech; and create a phonetic representation of the speech segment of the current speech sample; and
a back end module embodied in one or more non-transitory computer readable media and executable by at least one computer device to: create a current speaker model based on the phonetic representation of the speech segment of the current speech sample, the current speaker model mathematically representing at least one speaker-specific phonemic characteristic of the current speech sample; and compare the current speaker model to a stored speaker model, the stored speaker model mathematically associating phonetic content with one or more other speech samples;
wherein the front end module is to apply a neural network-based acoustic model to associate the speech segment with phonetic content;
wherein the front end module is to align the phonetic content of the speech segment with time; and
wherein the front end module is to align the phonetic content of the speech segment in lexical units, and the back end module is to compute a distance between at least one of the lexical units of the phonetic content with a similar lexical unit of the stored speaker model.

2. A text-independent speaker recognition system comprising:
a front end module embodied in one or more non-transitory computer readable media and executable by at least one computer device to: process an audio signal comprising a current sample of natural language speech; identify a speech segment in the current sample of natural language speech; and create a phonetic representation of the speech segment of the current speech sample; and
a back end module embodied in one or more non-transitory computer readable media and executable by at least one computer device to: create a current speaker model based on the phonetic representation of the speech segment of the current speech sample, the current speaker model mathematically representing at least one speaker-specific phonemic characteristic of the current speech sample; and compare the current speaker model to a stored speaker model, the stored speaker model mathematically associating phonetic content with one or more other speech samples;
wherein the front end module is to apply a neural network-based acoustic model to associate the speech segment with phonetic content;
wherein the front end module is to align the phonetic content of the speech segment with time; and
wherein the front end module is to align the phonetic content of the speech segment in tri-phones, and the back end module is to compute a distance between at least one of the tri-phones of the phonetic content with a similar tri-phone of the stored speaker model.

3. A text-independent speaker recognition system comprising:
a front end module embodied in one or more non-transitory computer readable media and executable by at least one computer device to: process an audio signal comprising a current sample of natural language speech; identify a speech segment in the current sample of natural language speech; and create a phonetic representation of the speech segment of the current speech sample; and
a back end module embodied in one or more non-transitory computer readable media and executable by at least one computer device to: create a current speaker model based on the phonetic representation of the speech segment of the current speech sample, the current speaker model mathematically representing at least one speaker-specific phonemic characteristic of the current speech sample; and compare the current speaker model to a stored speaker model, the stored speaker model mathematically associating phonetic content with one or more other speech samples;
wherein the front end module is to apply a neural network-based acoustic model to associate the speech segment with phonetic content;
wherein the front end module is to align the phonetic content of the speech segment with time;
wherein the front end module is to align the phonetic content of the speech segment in tri-phones, and the back end module is to compute a distance between at least one of the tri-phones of the phonetic content with a similar tri-phone of the stored speaker model; and
wherein the back end module is to disregard tri-phones of the speech segment that do not have similar tri-phones in the stored speaker model.

4. A front end module for a text-independent speaker recognition system, the front end module embodied in one or more non-transitory computer readable media and executable by at least one computer device comprising a plurality of instructions embodied in one or more computer accessible storage media and executable by a processor to:
process an audio signal comprising a sample of natural language speech;
identify a plurality of temporal speech segments in the natural language speech sample;
assign a phonetic unit of a plurality of different phonetic units to each of the speech segments of the speech sample, wherein each of the phonetic units is associated with a class of phonetic content of a plurality of classes of phonetic content; and
mathematically determine speaker-specific information about the pronunciation of the speech segments in comparison to the pronunciation of the speech segments by a general population;
wherein the front end module is to apply a partial speech recognition system comprising a hidden Markov model and a deep neural network to associate different speech segments with different phonetic units; and
wherein the front end module is to determine the phonetic unit to associate with a current speech segment based on a previously-determined association of a phonetic unit with another speech segment that is temporally adjacent the current speech segment in the natural language speech sample.

5. A front end module for a text-independent speaker recognition system, the front end module embodied in one or more non-transitory computer readable media and executable by at least one computer device comprising a plurality of instructions embodied in one or more computer accessible storage media and executable by a processor to:
process an audio signal comprising a sample of natural language speech;
identify a plurality of temporal speech segments in the natural language speech sample;
assign a phonetic unit of a plurality of different phonetic units to each of the speech segments of the speech sample, wherein each of the phonetic units is associated with a class of phonetic content of a plurality of classes of phonetic content; and
mathematically determine speaker-specific information about the pronunciation of the speech segments in comparison to the pronunciation of the speech segments by a general population;
wherein the front end module is to compute a plurality of statistics to determine the speaker-specific information, and the front end module is to compute the plurality of statistics using posterior probabilities of the phonetic classes.

6. A method for text-independent speaker recognition, the method comprising, with code embodied in one or more non-transitory computer readable media and executable by at least one computing device:
processing an audio signal comprising a current sample of natural language speech and speaker-specific information about the speaker of the current speech sample;
executing a speech recognizer on the current speech sample to: identify a speech segment in the current speech sample; and create a phonetic representation of the speech s

Assignees

• Stanford Res Inst Int

Inventors

• Scheffer Nicolas
• Lei Yun

Classifications

• G10L17/14 Primary

  Use of phonemic categorisation or speech recognition prior to speaker recognition or verification · CPC title