1.
J Acoust Soc Am
; 148(5): EL414, 2020 11.
Artigo
em Inglês
| MEDLINE
| ID: mdl-33261377
RESUMO
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.