  Speech enhancement (SE) is used as a frontend in speech applications
including automatic speech recognition (ASR) and telecommunication. A
difficulty in using the SE frontend is that the appropriate noise reduction
level differs depending on applications and/or noise characteristics. In this
study, we propose "signal-to-noise ratio improvement (SNRi) target training";
the SE frontend is trained to output a signal whose SNRi is controlled by an
auxiliary scalar input. In joint training with a backend, the target SNRi value
is estimated by an auxiliary network. By training all networks to minimize the
backend task loss, we can estimate the appropriate noise reduction level for
each noisy input in a data-driven scheme. Our experiments showed that the SNRi
target training enables control of the output SNRi. In addition, the proposed
joint training relatively reduces word error rate by 4.0\% and 5.7\% compared
to a Conformer-based standard ASR model and conventional SE-ASR joint training
model, respectively. Furthermore, by analyzing the predicted target SNRi, we
observed the jointly trained network automatically controls the target SNRi
according to noise characteristics. Audio demos are available in our demo page:
google.github.io/df-conformer/snri_target/.
