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Journal Article

Acoustic and linguistic features influence talker changedetection


Fink,  Lauren
Department of Music, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

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Sharma, N. K., Krishnamohan, V., Ganapathy, S., Gangopadhayay, A., & Fink, L. (2020). Acoustic and linguistic features influence talker changedetection. The Journal of the Acoustical Society of America, 148(5), EL414-EL419. doi:10.1121/10.0002462.

Cite as: https://hdl.handle.net/21.11116/0000-0007-A139-3
A listening test is proposed in which human participants detect talker changes in two natural, multi-talker speech stimuli sets—a familiar language (English) and an unfamiliar language (Chinese). Miss rate, false-alarm rate, and response times (RT) showed a significant dependence on language familiarity. Linear regression modeling of RTs using diverse acoustic features derived from the stimuli showed recruitment of a pool of acoustic features for the talker change detection task. Further, benchmarking the same task against the state-of-the-art machine diarization system showed that the machine system achieves human parity for the familiar language but not for the unfamiliar language.