  Zero-shot multi-speaker Text-to-Speech (TTS) generates target speaker voices
given an input text and the corresponding speaker embedding. In this work, we
investigate the effectiveness of the TTS reconstruction objective to improve
representation learning for speaker verification. We jointly trained end-to-end
Tacotron 2 TTS and speaker embedding networks in a self-supervised fashion. We
hypothesize that the embeddings will contain minimal phonetic information since
the TTS decoder will obtain that information from the textual input. TTS
reconstruction can also be combined with speaker classification to enhance
these embeddings further. Once trained, the speaker encoder computes
representations for the speaker verification task, while the rest of the TTS
blocks are discarded. We investigated training TTS from either manual or
ASR-generated transcripts. The latter allows us to train embeddings on datasets
without manual transcripts. We compared ASR transcripts and Kaldi phone
alignments as TTS inputs, showing that the latter performed better due to their
finer resolution. Unsupervised TTS embeddings improved EER by 2.06\% absolute
with regard to i-vectors for the LibriTTS dataset. TTS with speaker
classification loss improved EER by 0.28\% and 0.73\% absolutely from a model
using only speaker classification loss in LibriTTS and Voxceleb1 respectively.
