  Recently, there have been efforts to encode the linguistic information of
speech using a self-supervised framework for speech synthesis. However,
predicting representations from surrounding representations can inadvertently
entangle speaker information in the speech representation. This paper aims to
remove speaker information by exploiting the structured nature of speech,
composed of discrete units like phonemes with clear boundaries. A neural
network predicts these boundaries, enabling variable-length pooling for
event-based representation extraction instead of fixed-rate methods. The
boundary predictor outputs a probability for the boundary between 0 and 1,
making pooling soft. The model is trained to minimize the difference with the
pooled representation of the data augmented by time-stretch and pitch-shift. To
confirm that the learned representation includes contents information but is
independent of speaker information, the model was evaluated with libri-light's
phonetic ABX task and SUPERB's speaker identification task.
