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 language | English |
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Article number | 12 |
Journal | ACM Transactions on Speech and Language Processing |
Volume | 7 |
Issue number | 4 |
DOIs | |
State | Published - Aug 2011 |
Keywords
- Children's speech
- Dynamic bayesian networks
- Keyword spotting
- Long Short-Term Memory