Acoustic-linguistic recognition of interest in speech with bottleneck-BLSTM nets

This paper proposes a novel technique for speech-based interest recognition in natural conversations. We introduce a fully automatic system that exploits the principle of bidirectional Long Short-Term Memory (BLSTM) as well as the structure of socalled bottleneck networks. BLSTM nets are able to model a self-learned amount of context information, which was shown to be beneficial for affect recognition applications, while bottleneck networks allow for efficient feature compression within neural networks. In addition to acoustic features, our technique considers linguistic information obtained from a multi-stream BLSTM-HMM speech recognizer. Evaluations on the TUM AVIC corpus reveal that the bottleneck-BLSTM method prevails over all approaches that have been proposed for the Interspeech 2010 Paralinguistic Challenge task.