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Speaker diarization (SD) is typically used with an automatic speechrecognition (ASR) system to ascribe speaker labels to recognized words. Theconventional approach reconciles outputs from independently optimized ASR andSD systems, where the SD system typically uses only acoustic information toidentify the speakers in the audio stream. This approach can lead to speakererrors especially around speaker turns and regions of speaker overlap. In thispaper, we propose a novel second-pass speaker error correction system usinglexical information, leveraging the power of modern language models (LMs). Ourexperiments across multiple telephony datasets show that our approach is botheffective and robust. Training and tuning only on the Fisher dataset, thiserror correction approach leads to relative word-level diarization error rate(WDER) reductions of 15-30% on three telephony datasets: RT03-CTS, CallhomeAmerican English and held-out portions of Fisher.

Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction