Acoustic and linguistic features influence talker change detection | Zendy

Neeraj Kumar Sharma | Zendy; Venkat Krishnamohan | Zendy; Sriram Ganapathy | Zendy; Ahana Gangopadhayay | Zendy; Lauren K. Fink | Zendy

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Acoustic and linguistic features influence talker change detection

Author(s) -

Neeraj Kumar Sharma,

Venkat Krishnamohan,

Sriram Ganapathy,

Ahana Gangopadhayay,

Lauren K. Fink

Publication year - 2020

Publication title -

the journal of the acoustical society of america

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.619

H-Index - 187

eISSN - 1520-8524

pISSN - 0001-4966

DOI - 10.1121/10.0002462

Subject(s) - computer science , speech recognition , task (project management) , active listening , false alarm , first language , constant false alarm rate , natural language processing , artificial intelligence , linguistics , psychology , communication , engineering , philosophy , systems engineering

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.

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