  Recent work has explored using self-supervised learning (SSL) speech
representations such as wav2vec2.0 as the representation medium in standard
two-stage TTS, in place of conventionally used mel-spectrograms. It is however
unclear which speech SSL is the better fit for TTS, and whether or not the
performance differs between read and spontaneous TTS, the later of which is
arguably more challenging. This study aims at addressing these questions by
testing several speech SSLs, including different layers of the same SSL, in
two-stage TTS on both read and spontaneous corpora, while maintaining constant
TTS model architecture and training settings. Results from listening tests show
that the 9th layer of 12-layer wav2vec2.0 (ASR finetuned) outperforms other
tested SSLs and mel-spectrogram, in both read and spontaneous TTS. Our work
sheds light on both how speech SSL can readily improve current TTS systems, and
how SSLs compare in the challenging generative task of TTS. Audio examples can
be found at https://www.speech.kth.se/tts-demos/ssr_tts
