  Target-Speaker Voice Activity Detection (TS-VAD) utilizes a set of speaker
profiles alongside an input audio signal to perform speaker diarization. While
its superiority over conventional methods has been demonstrated, the method can
suffer from errors in speaker profiles, as those profiles are typically
obtained by running a traditional clustering-based diarization method over the
input signal. This paper proposes an extension to TS-VAD, called
Profile-Error-Tolerant TS-VAD (PET-TSVAD), which is robust to such speaker
profile errors. This is achieved by employing transformer-based TS-VAD that can
handle a variable number of speakers and further introducing a set of
additional pseudo-speaker profiles to handle speakers undetected during the
first pass diarization. During training, we use speaker profiles estimated by
multiple different clustering algorithms to reduce the mismatch between the
training and testing conditions regarding speaker profiles. Experimental
results show that PET-TSVAD consistently outperforms the existing TS-VAD method
on both the VoxConverse and DIHARD-I datasets.
