  Adapting generic speech recognition models to specific individuals is a
challenging problem due to the scarcity of personalized data. Recent works have
proposed boosting the amount of training data using personalized text-to-speech
synthesis. Here, we ask two fundamental questions about this strategy: when is
synthetic data effective for personalization, and why is it effective in those
cases? To address the first question, we adapt a state-of-the-art automatic
speech recognition (ASR) model to target speakers from four benchmark datasets
representative of different speaker types. We show that ASR personalization
with synthetic data is effective in all cases, but particularly when (i) the
target speaker is underrepresented in the global data, and (ii) the capacity of
the global model is limited. To address the second question of why personalized
synthetic data is effective, we use controllable speech synthesis to generate
speech with varied styles and content. Surprisingly, we find that the text
content of the synthetic data, rather than style, is important for speaker
adaptation. These results lead us to propose a data selection strategy for ASR
personalization based on speech content.
