LANGUAGE-AGNOSTIC MULTILINGUAL MODELING USING EFFECTIVE SCRIPT NORMALIZATION

A method (600) includes obtaining a plurality of training data sets (202) each associated with a respective native language and includes a plurality of respective training data samples (204). For each respective training data sample of each training data set in the respective native language, the me...

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Bibliographic Details
Main Authors ROAK, Brian, RAMABHADRAN, Bhuvana, DATTA, Arindrima, EMOND, Jesse
Format Patent
LanguageEnglish
French
German
Published 01.05.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A method (600) includes obtaining a plurality of training data sets (202) each associated with a respective native language and includes a plurality of respective training data samples (204). For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text (121) representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio (210) in the respective native language to generate a respective normalized training data sample (240). The method also includes training, using the normalized training data samples, a multilingual model (300) to predict speech recognition results (120) in the target script for corresponding speech utterances (106) spoken in any of the different native languages associated with the plurality of training data sets.
Bibliography:Application Number: EP20240162746