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.

通过利用语音的结构性质，采用神经网络预测边界从而实现可变长度池化，以去除语音表示中的说话人信息。通过使用时间拉伸和音高变换的数据增强方法，该模型训练时被评估为包含内容信息但独立于说话人信息。