  The IEEE Spoken Language Technology Workshop (SLT) 2021 Alpha-mini Speech
Challenge (ASC) is intended to improve research on keyword spotting (KWS) and
sound source location (SSL) on humanoid robots. Many publications report
significant improvements in deep learning based KWS and SSL on open source
datasets in recent years. For deep learning model training, it is necessary to
expand the data coverage to improve the robustness of model. Thus, simulating
multi-channel noisy and reverberant data from single-channel speech, noise,
echo and room impulsive response (RIR) is widely adopted. However, this
approach may generate mismatch between simulated data and recorded data in real
application scenarios, especially echo data. In this challenge, we open source
a sizable speech, keyword, echo and noise corpus for promoting data-driven
methods, particularly deep-learning approaches on KWS and SSL. We also choose
Alpha-mini, a humanoid robot produced by UBTECH equipped with a built-in
four-microphone array on its head, to record development and evaluation sets
under the actual Alpha-mini robot application scenario, including noise as well
as echo and mechanical noise generated by the robot itself for model
evaluation. Furthermore, we illustrate the rules, evaluation methods and
baselines for researchers to quickly assess their achievements and optimize
their models.
