On The Impact of Language Familiarity in Talker Change Detection

Sharma, Neeraj; Krishnamohan, Venkat; Ganapathy, Sriram; Gangopadhayay, Ahana; Fink, Lauren

doi:10.1109/icassp40776.2020.9054294

Cited by 1 publication

(3 citation statements)

References 26 publications

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“…We note that the subject pool recruited for the study consisted of non-native English speakers, proficient in English. Data from our past study (Sharma et al, 2020) and also from K€ oster and Schiller (1997) suggests non-nativeness does not have an impact on talker perception tasks, though future studies may wish to manipulate this factor.…”

Section: Discussionmentioning

confidence: 73%

“…The study presented here is an extension of our work in (Sharma et al, 2020a) with a larger set of human participants, and a detailed analysis of reaction time modeling.…”

Section: Methodsmentioning

confidence: 99%

“…The experiment was conducted in an isolated sound booth using high fidelity headphones (Sensheiser HD 215). A graphical user interface designed in python and HTML was used for stimuli presentation and recording responses [stimulus material available at Sharma et al (2020b)]. After presentation of a stimulus, the listener responded with a button press indicating the number of talkers (1 or 2).…”

Section: Listening Testmentioning

confidence: 99%

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Acoustic and linguistic features influence talker change detection

Sharma

Krishnamohan

Ganapathy

et al. 2020

The Journal of the Acoustical Society of America

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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.

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Section: Discussionmentioning

confidence: 73%

“…The study presented here is an extension of our work in (Sharma et al, 2020a) with a larger set of human participants, and a detailed analysis of reaction time modeling.…”

Section: Methodsmentioning

confidence: 99%

Section: Listening Testmentioning

confidence: 99%

See 1 more Smart Citation