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        <title>The Influence of the Utterance Length on the Recognition of Aged Voices</title>
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    <abstract>This paper addresses the recognition of elderly callers based on short and narrow-band utterances, which are typical for Interactive Voice Response (IVR) systems. Our study is based on 2308 short utterances from a deployed IVR application. We show that features such as speaking rate, jitter and shimmer that are considered as most meaningful ones for determining elderly users underperform when used in the IVR context while pitch and intensity features seem to gain importance. We further demonstrate the influence of the utterance length on the classifier’s performance: for both humans and classifier, the distinction between aged and non-aged voices becomes increasingly difficult the shorter the utterances get. Our setup based on a Support Vector Machine (SVM) with linear kernel reaches a comparably poor performance of 58% accuracy, which can be attributed to an average utterance length of only 1.6 seconds. The automatic distinction between aged and non-aged utterances drops to random when the utterance length falls below 1.2 seconds.</abstract>
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%0 Conference Proceedings
%T The Influence of the Utterance Length on the Recognition of Aged Voices
%A Schmitt, Alexander
%A Polzehl, Tim
%A Minker, Wolfgang
%A Liscombe, Jackson
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F schmitt-etal-2010-influence
%X This paper addresses the recognition of elderly callers based on short and narrow-band utterances, which are typical for Interactive Voice Response (IVR) systems. Our study is based on 2308 short utterances from a deployed IVR application. We show that features such as speaking rate, jitter and shimmer that are considered as most meaningful ones for determining elderly users underperform when used in the IVR context while pitch and intensity features seem to gain importance. We further demonstrate the influence of the utterance length on the classifier’s performance: for both humans and classifier, the distinction between aged and non-aged voices becomes increasingly difficult the shorter the utterances get. Our setup based on a Support Vector Machine (SVM) with linear kernel reaches a comparably poor performance of 58% accuracy, which can be attributed to an average utterance length of only 1.6 seconds. The automatic distinction between aged and non-aged utterances drops to random when the utterance length falls below 1.2 seconds.
%U https://aclanthology.org/L10-1102/

Markdown (Informal)

[The Influence of the Utterance Length on the Recognition of Aged Voices](https://aclanthology.org/L10-1102/) (Schmitt et al., LREC 2010)

• The Influence of the Utterance Length on the Recognition of Aged Voices (Schmitt et al., LREC 2010)

ACL

• Alexander Schmitt, Tim Polzehl, Wolfgang Minker, and Jackson Liscombe. 2010. The Influence of the Utterance Length on the Recognition of Aged Voices. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).