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Conference Paper

On the impact of language familiarity in talker change detection


Fink,  Lauren
Department of Music, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Center for Mind and Brain, Univ. of California;

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Sharma, N., Krishnamohan, V., Ganapathy, S., Gangopadhayay, A., & Fink, L. (2020). On the impact of language familiarity in talker change detection. In The Institute of Electrical and Electronics EngineersSignal Processing Society (Ed.), 2020 IEEE InternationalConference on Acoustics, Speech,and Signal Processing: Proceedings (pp. 6249-6253). doi:10.1109/ICASSP40776.2020.9054294.

Cite as: https://hdl.handle.net/21.11116/0000-0008-3FDC-B
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.