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

Martin Wöllmer, Björn Schuller, Anton Batliner, Stefan Steidl, Dino Seppi

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish
Article number12
JournalACM Transactions on Speech and Language Processing
Volume7
Issue number4
DOIs
StatePublished - Aug 2011

Keywords

  • Children's speech
  • Dynamic bayesian networks
  • Keyword spotting
  • Long Short-Term Memory

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