On The Impact of Language Familiarity in Talker Change Detection
Neeraj Kumar Sharma, Venkat Krishnamohan, Sriram Ganapathy, Ahana Gangopadhayay, Lauren K. Fink
Abstract
The ability to detect talker changes when listening to conversational speech is fundamental to perception and understanding of multi-talker speech. In this paper, we propose an experimental paradigm to provide insights on the impact of language familiarity on talker change detection. Two multi-talker speech stimulus sets, one in a language familiar to the listeners (English) and the other unfamiliar (Chinese), are created. A listening test is performed in which listeners indicate the number of talkers in the presented stimuli. Analysis of human performance shows statistically significant results for: (a) lower miss (and a higher false alarm) rate in familiar versus unfamiliar language, and (b) longer response time in familiar versus unfamiliar language. These results signify a link between perception of talker attributes and language proficiency. Subsequently, a machine system is designed to perform the same task. The system makes use of the current state-of-the-art diarization approach with x-vector embeddings. A performance comparison on the same stimulus set indicates that the machine system falls short of human performance by a huge margin, for both languages.
BibTeX
@inproceedings{icassp2020_ontheimpactoflan,
title = {On The Impact of Language Familiarity in Talker Change Detection},
author = {Neeraj Kumar Sharma and Venkat Krishnamohan and Sriram Ganapathy and Ahana Gangopadhayay and Lauren K. Fink},
booktitle = {ICASSP 2020},
year = {2020}
}