The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 11, 2019

Filed:

May. 17, 2018
Applicant:

Telepathy Labs, Inc., Tampa, FL (US);

Inventors:

Martin Reber, Remetschwil, CH;

Vijeta Avijeet, Dübendorf, CH;

Assignee:

Telepathy Labs, Inc., Tampa, FL (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G10L 13/08 (2013.01); G06K 9/62 (2006.01); G06N 5/02 (2006.01); G06N 3/02 (2006.01); G10L 19/00 (2013.01); G10L 13/04 (2013.01);
U.S. Cl.
CPC ...
G10L 13/08 (2013.01); G06K 9/6247 (2013.01); G06K 9/6298 (2013.01); G06N 3/02 (2013.01); G06N 5/02 (2013.01); G10L 13/04 (2013.01); G10L 19/00 (2013.01);
Abstract

A technique improves training and speech quality of a text-to-speech (TTS) system having an artificial intelligence, such as a neural network. The TTS system is organized as a front-end subsystem and a back-end subsystem. The front-end subsystem is configured to provide analysis and conversion of text into input vectors, each having at least a base frequency, f, a phenome duration, and a phoneme sequence that is processed by a signal generation unit of the back-end subsystem. The signal generation unit includes the neural network interacting with a pre-existing knowledgebase of phenomes to generate audible speech from the input vectors. The technique applies an error signal from the neural network to correct imperfections of the pre-existing knowledgebase of phenomes to generate audible speech signals. Speech signal specific modelling techniques in combination with applied psychoacoustic principles drive training efficiency of neural networks with positive impact on quality of generated speech signals.


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