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"Privacy preservation calls for speech anonymization methods which hide the speaker s identity while minimizing the impact on downstream tasks such as automatic speech recognition  ASR  training or decoding  In the recent VoicePrivacy 2020 Challenge  several anonymization methods have been proposed to transform speech utterances in a way that preserves their verbal and prosodic contents while reducing the accuracy of a speaker verification system  In this paper  we propose to further increase the privacy achieved by such methods by segmenting the utterances into shorter slices  We show that our approach has two major impacts on privacy  First  it reduces the accuracy of speaker verification with respect to unsegmented utterances  Second  it also reduces the amount of personal information that can be extracted from the verbal content  in a way that cannot easily be reversed by an attacker  We also show that it is possible to train an ASR system from anonymized speech slices with negligible impact on the word error rate "
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