Tandem decoding of children's speech for keyword detection in a child-robot interaction scenario

Authors:
Martin Wöllmer;Björn Schuller;Anton Batliner;Stefan Steidl;Dino Seppi
Affiliations:
Technische Universität München, Germany;Technische Universität München, Germany;Friedrich-Alexander-Universität Erlangen-Nürnberg;Friedrich-Alexander-Universität Erlangen-Nürnberg;Katholieke Universiteit Leuven
Venue:
ACM Transactions on Speech and Language Processing (TSLP)
Year:
2011

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Abstract

In this article, we focus on keyword detection in children's speech as it is needed in voice command systems. We use the FAU Aibo Emotion Corpus which contains emotionally colored spontaneous children's speech recorded in a child-robot interaction scenario and investigate various recent keyword spotting techniques. As the principle of bidirectional Long Short-Term Memory (BLSTM) is known to be well-suited for context-sensitive phoneme prediction, we incorporate a BLSTM network into a Tandem model for flexible coarticulation modeling in children's speech. Our experiments reveal that the Tandem model prevails over a triphone-based Hidden Markov Model approach.