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Impact of cognitive load and frustration on drivers’ speech.
Author(s) -
Hynek Bořil,
Tristan Kleinschmidt,
Pınar Boyraz,
John H. L. Hansen
Publication year - 2010
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 - 10.1121/1.3385171
Subject(s) - distraction , mel frequency cepstrum , computer science , speech recognition , cognition , cognitive load , artificial intelligence , psychology , cognitive psychology , feature extraction , neuroscience
Secondary tasks such as cell phone calls or interaction with automated speech dialog systems (SDSs) increase the driver’s cognitive load as well as the probability of driving errors. This study analyzes speech production variations due to cognitive load and emotional state of drivers in real driving conditions. Speech samples were acquired from 24 female and 17 male subjects (approximately 8.5 h of data) while talking to a co-driver and communicating with two automated call centers, with emotional states (neutral, negative) and the\udnumber of necessary SDS query repetitions also labeled. A consistent shift in a number of speech production parameters (pitch, first format center frequency, spectral center of gravity, spectral energy spread, and duration of voiced segments) was observed when comparing\udSDS interaction against co-driver interaction; further increases were observed when considering negative emotion segments and the number of requested SDS query repetitions. A mel frequency cepstral coefficient based Gaussian mixture classifier trained on 10 male and 10 female sessions provided 91% accuracy in the open test set task of distinguishing co-driver interactions from SDS interactions,\udsuggesting—together with the acoustic analysis—that it is possible to monitor the level of driver distraction directly from their speech

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