Journal
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
Volume 148, Issue 5, Pages EL414-EL419Publisher
ACOUSTICAL SOC AMER AMER INST PHYSICS
DOI: 10.1121/10.0002462
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- National Science Foundation (NSF)
- British Telecom India Research Center (BTIRC)
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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. (C) 2020 Acoustical Society of America.
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