Method for personalization of ASR models

A user device comprising:

• a memory storing one or more instructions;
• and
• at least one processor configured to execute the one or more instructions to:
• obtain a pre-trained automatic speech recognition (ASR) model,
• obtain a user data from a user database,
• analyse a distribution of the user data with respect to an acoustic characteristic,
• determine, 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, divide 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,
• apply data augmentation to add the acoustic characteristic in the training subset, and
• update the pre-trained ASR model with the augmented training subset to generate a personalised local machine learning (ML) model.

A non-transitory data carrier carrying code which, when implemented on a processor of a user device, causes the user device to perform operations, the operations comprising:

• 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, wherein the acoustic characteristic is less audible in the training subset than in the validation subset;
• applying data augmentation to add the acoustic characteristic to the training subset;
• and
• updating the pre-trained ASR model with the augmented training subset to generate a personalised local machine learning (ML) model.