  This paper describes a hands-on comparison on using state-of-the-art music
source separation deep neural networks (DNNs) before and after task-specific
fine-tuning for separating speech content from non-speech content in broadcast
audio (i.e., dialog separation). The music separation models are selected as
they share the number of channels (2) and sampling rate (44.1 kHz or higher)
with the considered broadcast content, and vocals separation in music is
considered as a parallel for dialog separation in the target application
domain. These similarities are assumed to enable transfer learning between the
tasks. Three models pre-trained on music (Open-Unmix, Spleeter, and
Conv-TasNet) are considered in the experiments, and fine-tuned with real
broadcast data. The performance of the models is evaluated before and after
fine-tuning with computational evaluation metrics (SI-SIRi, SI-SDRi, 2f-model),
as well as with a listening test simulating an application where the non-speech
signal is partially attenuated, e.g., for better speech intelligibility. The
evaluations include two reference systems specifically developed for dialog
separation. The results indicate that pre-trained music source separation
models can be used for dialog separation to some degree, and that they benefit
from the fine-tuning, reaching a performance close to task-specific solutions.
