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Streaming voice conversion (VC) is the task of converting the voice of oneperson to another in real-time. Previous streaming VC methods use phoneticposteriorgrams (PPGs) extracted from automatic speech recognition (ASR) systemsto represent speaker-independent information. However, PPGs lack the prosodyand vocalization information of the source speaker, and streaming PPGs containundesired leaked timbre of the source speaker. In this paper, we propose to useintermediate bottleneck features (IBFs) to replace PPGs. VC systems trainedwith IBFs retain more prosody and vocalization information of the sourcespeaker. Furthermore, we propose a non-streaming teacher guidance (TG)framework that addresses the timbre leakage problem. Experiments show that ourproposed IBFs and the TG framework achieve a state-of-the-art streaming VCnaturalness of 3.85, a content consistency of 3.77, and a timbre similarity of3.77 under a future receptive field of 160 ms which significantly outperformprevious streaming VC systems.

Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance