Speaker diarization assisted ASR for multi-speaker conversations
Year: 2021
Type: preprint
Abstract: In this paper, we propose a novel approach for the transcription of speech conversations with natural speaker overlap, from single channel recordings. We propose a combination of a speaker diarization system and a hybrid automatic speech recognition (ASR) system with speaker activity assisted acoustic model (AM). An end-to-end neural network system is used for speaker diarization. Two architectures, (i) input conditioned AM, and (ii) gated features AM, are explored to incorporate the speaker activity information. The models output speaker specific senones. The experiments on Switchboard telephone conversations show the advantage of incorporating speaker activity information in the ASR system for recordings with overlapped speech. In particular, an absolute improvement of $11\%$ in word error rate (WER) is seen for the proposed approach on natural conversation speech with automatic diarization.
Source: arXiv (Cornell University)
Authors: Srikanth Raj Chetupalli, Sriram Ganapathy
Institution: Indian Institute of Science Bangalore
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3148993286