Method for personalization of asr models

Abstract

The disclosure generally relate to a method performed by a user device obtaining a pre-trained automatic speech recognition (ASR) model, obtaining a user data from a user database, analysing a distribution of the user data with respect to an acoustic characteristic, determining, using the distribution, whether data augmentation for the acoustic characteristic is to be applied, when it is determined that data augmentation is to be applied, dividing the user data into a training subset and a validation subset, based on an acoustic characteristic being less audible in the training subset than in the validation subset, applying data augmentation to add the acoustic characteristic to the user data in the training subset, and updating the pre-trained ASR model with the augmented training subset to generate a personalised local ML model.