  In this paper, we propose a novel spoken-text-style conversion method that
can simultaneously execute multiple style conversion modules such as
punctuation restoration and disfluency deletion without preparing matched
datasets. In practice, transcriptions generated by automatic speech recognition
systems are not highly readable because they often include many disfluencies
and do not include punctuation marks. To improve their readability, multiple
spoken-text-style conversion modules that individually model a single
conversion task are cascaded because matched datasets that simultaneously
handle multiple conversion tasks are often unavailable. However, the cascading
is unstable against the order of tasks because of the chain of conversion
errors. Besides, the computation cost of the cascading must be higher than the
single conversion. To execute multiple conversion tasks simultaneously without
preparing matched datasets, our key idea is to distinguish individual
conversion tasks using the on-off switch. In our proposed zero-shot joint
modeling, we switch the individual tasks using multiple switching tokens,
enabling us to utilize a zero-shot learning approach to executing simultaneous
conversions. Our experiments on joint modeling of disfluency deletion and
punctuation restoration demonstrate the effectiveness of our method.
