  Streaming voice conversion (VC) is the task of converting the voice of one
person to another in real-time. Previous streaming VC methods use phonetic
posteriorgrams (PPGs) extracted from automatic speech recognition (ASR) systems
to represent speaker-independent information. However, PPGs lack the prosody
and vocalization information of the source speaker, and streaming PPGs contain
undesired leaked timbre of the source speaker. In this paper, we propose to use
intermediate bottleneck features (IBFs) to replace PPGs. VC systems trained
with IBFs retain more prosody and vocalization information of the source
speaker. Furthermore, we propose a non-streaming teacher guidance (TG)
framework that addresses the timbre leakage problem. Experiments show that our
proposed IBFs and the TG framework achieve a state-of-the-art streaming VC
naturalness of 3.85, a content consistency of 3.77, and a timbre similarity of
3.77 under a future receptive field of 160 ms which significantly outperform
previous streaming VC systems.
