  Recent speaker diarisation systems often convert variable length speech
segments into fixed-length vector representations for speaker clustering, which
are known as speaker embeddings. In this paper, the content-aware speaker
embeddings (CASE) approach is proposed, which extends the input of the speaker
classifier to include not only acoustic features but also their corresponding
speech content, via phone, character, and word embeddings. Compared to
alternative methods that leverage similar information, such as multitask or
adversarial training, CASE factorises automatic speech recognition (ASR) from
speaker recognition to focus on modelling speaker characteristics and
correlations with the corresponding content units to derive more expressive
representations. CASE is evaluated for speaker re-clustering with a realistic
speaker diarisation setup using the AMI meeting transcription dataset, where
the content information is obtained by performing ASR based on an automatic
segmentation. Experimental results showed that CASE achieved a 17.8% relative
speaker error rate reduction over conventional methods.
