  It is challenging to improve automatic speech recognition (ASR) performance
in noisy conditions with a single-channel speech enhancement (SE) front-end.
This is generally attributed to the processing distortions caused by the
nonlinear processing of single-channel SE front-ends. However, the causes of
such degraded ASR performance have not been fully investigated. How to design
single-channel SE front-ends in a way that significantly improves ASR
performance remains an open research question. In this study, we investigate a
signal-level numerical metric that can explain the cause of degradation in ASR
performance. To this end, we propose a novel analysis scheme based on the
orthogonal projection-based decomposition of SE errors. This scheme manually
modifies the ratio of the decomposed interference, noise, and artifact errors,
and it enables us to directly evaluate the impact of each error type on ASR
performance. Our analysis reveals the particularly detrimental effect of
artifact errors on ASR performance compared to the other types of errors. This
provides us with a more principled definition of processing distortions that
cause the ASR performance degradation. Then, we study two practical approaches
for reducing the impact of artifact errors. First, we prove that the simple
observation adding (OA) post-processing (i.e., interpolating the enhanced and
observed signals) can monotonically improve the signal-to-artifact ratio.
Second, we propose a novel training objective, called artifact-boosted
signal-to-distortion ratio (AB-SDR), which forces the model to estimate the
enhanced signals with fewer artifact errors. Through experiments, we confirm
that both the OA and AB-SDR approaches are effective in decreasing artifact
errors caused by single-channel SE front-ends, allowing them to significantly
improve ASR performance.
