Sharma, NK and Krishnamohan, V and Ganapathy, S and Gangopadhayay, A and Fink, L (2020) Acoustic and linguistic features influence talker change detection. In: Journal of the Acoustical Society of America, 148 (5). EL414-EL419.
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Abstract
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. © 2020 Acoustical Society of America.
| Item Type: | Journal Article |
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| Publication: | Journal of the Acoustical Society of America |
| Publisher: | Acoustical Society of America |
| Additional Information: | The copyright for this article belongs to The Authors. |
| Keywords: | Linguistics, Acoustic features; Change detection; False alarm rate; Linear regression models; Linguistic features; Listening tests; Machine systems; State of the art, Feature extraction, adult; article; benchmarking; female; human; human experiment; language; linear regression analysis; male; parity; reaction time; speech |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 10 Jan 2023 11:59 |
| Last Modified: | 10 Jan 2023 11:59 |
| URI: | https://eprints.iisc.ac.in/id/eprint/79027 |
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