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Many of the recent advances in speech separation are primarily aimed atsynthetic mixtures of short audio utterances with high degrees of overlap. Mostof these approaches need an additional stitching step to stitch the separatedspeech chunks for long form audio. Since most of the approaches involvePermutation Invariant training (PIT), the order of separated speech chunks isnondeterministic and leads to difficulty in accurately stitching homogenousspeaker chunks for downstream tasks like Automatic Speech Recognition (ASR).Also, most of these models are trained with synthetic mixtures and do notgeneralize to real conversational data. In this paper, we propose a speakerconditioned separator trained on speaker embeddings extracted directly from themixed signal using an over-clustering based approach. This model naturallyregulates the order of the separated chunks without the need for an additionalstitching step. We also introduce a data sampling strategy with real andsynthetic mixtures which generalizes well to real conversation speech. Withthis model and data sampling technique, we show significant improvements inspeaker-attributed word error rate (SA-WER) on Hub5 data.

Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech