  Modern smart glasses leverage advanced audio sensing and machine learning
technologies to offer real-time transcribing and captioning services,
considerably enriching human experiences in daily communications. However, such
systems frequently encounter challenges related to environmental noises,
resulting in degradation to speech recognition and speaker change detection. To
improve voice quality, this work investigates directional source separation
using the multi-microphone array. We first explore multiple beamformers to
assist source separation modeling by strengthening the directional properties
of speech signals. In addition to relying on predetermined beamformers, we
investigate neural beamforming in multi-channel source separation,
demonstrating that automatic learning directional characteristics effectively
improves separation quality. We further compare the ASR performance leveraging
separated outputs to noisy inputs. Our results show that directional source
separation benefits ASR for the wearer but not for the conversation partner.
Lastly, we perform the joint training of the directional source separation and
ASR model, achieving the best overall ASR performance.
