Abstract

The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding. That way, we avoid pruning the lattice too early, eliminating the potential risk of information loss.

Date

2019

Authors

Nikolaos Flemotomos, Panayiotis Georgiou, David C Atkins, Shrikanth Narayanan

Conference

ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

7330-7334

Publisher

IEEE