  We propose a modular pipeline for the single-channel separation, recognition,
and diarization of meeting-style recordings and evaluate it on the Libri-CSS
dataset. Using a Continuous Speech Separation (CSS) system with a TF-GridNet
separation architecture, followed by a speaker-agnostic speech recognizer, we
achieve state-of-the-art recognition performance in terms of Optimal Reference
Combination Word Error Rate (ORC WER). Then, a d-vector-based diarization
module is employed to extract speaker embeddings from the enhanced signals and
to assign the CSS outputs to the correct speaker. Here, we propose a
syntactically informed diarization using sentence- and word-level boundaries of
the ASR module to support speaker turn detection. This results in a
state-of-the-art Concatenated minimum-Permutation Word Error Rate (cpWER) for
the full meeting recognition pipeline.
