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. 10, 2025

Filed:

Jan. 13, 2020
Applicant:

Applications Technology (Apptek), Llc, McLean, VA (US);

Inventors:

Evgeny Matusov, Aachen, DE;

Jintao Jiang, Great Falls, VA (US);

Mudar Yaghi, McLean, VA (US);

Assignee:
Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 40/58 (2020.01); G06F 18/214 (2023.01); G06F 40/42 (2020.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G10L 13/00 (2006.01); G10L 25/90 (2013.01);
U.S. Cl.
CPC ...
G06F 40/58 (2020.01); G06F 18/214 (2023.01); G06F 40/42 (2020.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G10L 13/00 (2013.01); G10L 25/90 (2013.01);
Abstract

A system for translating speech from at least two source languages into another target language provides direct speech to target language translation. The target text is converted to speech in the target language through a TTS system. The system simplifies speech recognition and translation process by providing direct translation, includes mechanisms described herein that facilitate mixed language source speech translation, and punctuating output text streams in the target language. It also in some embodiments allows translation of speech into the target language to reflect the voice of the speaker of the source speech based on characteristics of the source language speech and speaker's voice and to produce subtitled data in the target language corresponding to the source speech. The system uses models having been trained using (i) encoder-decoder architectures with attention mechanisms and training data using TTS and (ii) parallel text training data in more than two different languages.


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