Module Comparison Of Transformer-Tts For Speaker Adaptation Based On Fine-Tuning

End-to-end text-to-speech (TTS) models have achieved remarkable results in recent times. However, the model requires a large amount of text and audio data for training. A speaker adaptation method based on fine-tuning has been proposed for constructing a TTS model using small scale data. Although these methods can replicate the target speaker s voice quality, synthesized speech includes the deletion and/or repetition of speech. The goal of speaker adaptation is to change the voice quality to match the target speaker ' s on the premise that adjusting the necessary modules will reduce the amount of data to be fine-tuned. In this paper, we clarify the role of each module in the Transformer-TTS process by not updating it. Specifically, we froze character embedding, encoder, layer predicting stop token, and loss function for estimating sentence ending. The experimental results showed the following: (1) fine-tuning the character embedding did not result in an improvement in the deletion and/or repetition of speech, (2) speech deletion increases if the encoder is not fine-tuned, (3) speech deletion was suppressed when the layer predicting stop token is not fine-tuned, and (4) there are frequent speech repetitions at sentence end when the loss function estimating sentence ending is omitted.