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There are significant challenges for speaker adaptation in text-to-speech forlanguages that are not widely spoken or for speakers with accents or dialectsthat are not well-represented in the training data. To address this issue, wepropose the use of the "mixture of adapters" method. This approach involvesadding multiple adapters within a backbone-model layer to learn the uniquecharacteristics of different speakers. Our approach outperforms the baseline,with a noticeable improvement of 5% observed in speaker preference tests whenusing only one minute of data for each new speaker. Moreover, following theadapter paradigm, we fine-tune only the adapter parameters (11% of the totalmodel parameters). This is a significant achievement in parameter-efficientspeaker adaptation, and one of the first models of its kind. Overall, ourproposed approach offers a promising solution to the speech synthesistechniques, particularly for adapting to speakers from diverse backgrounds.

ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation