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Acoustic Feature Comparison for Different Speaking Rates

Sabzi Shahrebabaki, Abdolreza; Imran, Ali Shariq; Olfati, Negar; Svendsen, Torbjørn Karl
Chapter
Accepted version
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Sabzi Shahrebabaki (252.9Kb)
URI
http://hdl.handle.net/11250/2598838
Date
2018
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  • Institutt for elektroniske systemer [1865]
  • Publikasjoner fra CRIStin - NTNU [26751]
Original version
10.1007/978-3-319-91250-9_14
Abstract
This paper investigates the effect of speaking rate variation on the task of frame classification. This task is indicative of the performance on phoneme and word recognition and is a first step towards designing voice-controlled interfaces. Different speaking rates cause different dynamics. For example, speaking rate variations will cause changes both in formant frequencies and in their transition tracks. A word spoken at normal speed gets recognized more often than the same word spoken by the same speaker at a much faster or slower pace, or vice-versa. It is thus imperative to design interfaces which take into account different speaking variabilities. To better incorporate speaker variability into digital devices, we study the effect of a) feature selection and b) the choice of network architecture on variable speaking rates. Four different features are evaluated on multiple configurations of Deep Neural Network (DNN) architectures. The findings show that log Filter-Bank Energies (FBE) outperformed the other acoustic features not only on normal speaking rate but for slow and fast speaking rates as well.
Publisher
Springer Verlag

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