  Recent work has shown that it is possible to train a single model to perform
joint acoustic echo cancellation (AEC), speech enhancement, and voice
separation, thereby serving as a unified frontend for robust automatic speech
recognition (ASR). The joint model uses contextual information, such as a
reference of the playback audio, noise context, and speaker embedding. In this
work, we propose a number of novel improvements to such a model. First, we
improve the architecture of the Cross-Attention Conformer that is used to
ingest noise context into the model. Second, we generalize the model to be able
to handle varying lengths of noise context. Third, we propose Signal Dropout, a
novel strategy that models missing contextual information. In the absence of
one or more signals, the proposed model performs nearly as well as
task-specific models trained without these signals; and when such signals are
present, our system compares well against systems that require all context
signals. Over the baseline, the final model retains a relative word error rate
reduction of 25.0% on background speech when speaker embedding is absent, and
61.2% on AEC when device playback is absent.
