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Lexical Emphasis Detection in Spoken French using F-BANKs and neural networks

Abstract : Expressiveness and non-verbal information in speech are active research topics in speech processing. In this work, we are interested in detecting emphasis at word-level as a mean to identify what are the focus words in a given utterance. We compare several machine learning techniques (Linear Discriminant Analysis, Support Vector Machines, Neural Networks) for this task carried out on SIWIS, a French speech synthesis database. Our approach consists first in aligning the spoken words to the speech signal and second to feed classifier with filter bank coefficients in order to take a binary decision at word-level: neutral/emphasized. Evaluation results show that a three-layer neural network performed best with a 93% accuracy.
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Submitted on : Thursday, April 30, 2020 - 5:34:11 PM
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  • HAL Id : hal-02559768, version 1
  • OATAO : 22280

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Abdelwahab Heba, Thomas Pellegrini, Tom Jorquera, Régine André-Obrecht, Jean-Pierre Lorré. Lexical Emphasis Detection in Spoken French using F-BANKs and neural networks. International Conference on Statistical Language and Speech Processing (SLSP 2017), Oct 2017, Le Mans, France. pp.241-249. ⟨hal-02559768⟩

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