Learning Pronunciation from Other Accents via Pronunciation Knowledge Transfer

Bootstrapping has proven to be effective in transforming a conventional pipeline-based linguistic frontend to an integrated Sequence-to-Sequence (Seq2Seq) frontend for text-to-speech (TTS). However, for target accents with limited lexical coverage, the performance of bootstrapped Seq2Seq frontends would be greatly limited. In this work, we utilize multi-accent bootstrapping for rich-resource source accents and low-resource target accents to enable pronunciation knowledge transfer between them, effectively enlarging the lexical coverage of target accent. We formally analyze the effect of transfer between 3 English accents (word accuracy increase of 12%–17% absolute for transferred words) and how it scales with the number of annotated unique word types in the target accent. When annotating as few as 1k word types for the target accent, the transfer achieves a word accuracy of 81% for transferred words, approaching the generalisation ability of a baseline annotating 51k word types.