Caller identification by voice

Witkowski, Marcin; Igras, Magdalena; Grzybowska, J; Jaciow, Pawel; Gałka, Jakub; Ziółko, Mariusz

doi:10.1109/pvc.2014.6845420

Cited by 1 publication

(1 citation statement)

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“…The cues about the age of a person can be observed in the voice due to numerous anatomical and physiological aspects that change during our lifetime [1]. Learning about the age of the speaker fulfills the profile of a speaker [2][3][4][5]. Automatic speaker age recognition can have forensic and commercial applications, e. g. narrowing down suspects or adjusting the performance of an Interactive Voice Response system to a specific age group.…”

Section: Introductionmentioning

confidence: 99%

Speaker Age Classification and Regression Using i-Vectors

Grzybowska

Kacprzak

2016

Interspeech 2016

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In this paper, we examine the use of i-vectors both for age regression as well as for age classification. Although i-vectors have been previously used for age regression task, we extend this approach by applying fusion of i-vectors and acoustic features regression to estimate the speaker age. By our fusion we obtain a relative improvement of 12.6% comparing to solely ivector system.We also use i-vectors for age classification, which to our knowledge is the first attempt to do so. Our best results reach unweighted accuracy 62.9%, which is a relative improvement of 16.7% comparing to the best results obtained in age classification task at Age Sub-Challenge at Interspeech 2010. Index Terms: speaker age recognition, regression, classification, computational paralinguistics Acoustic features for age estimationFeature sets were extracted with openSMILE [14] and consist of 450 features for each utterance in aGender corpus [15]. Those acoustic, prosodic and voice quality features are low-level descriptors (e. g. MFCCs, LSP Frequency, F0, voicing probability,

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Section: Introductionmentioning

confidence: 99%