15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

BioKIT — Real-Time Decoder for Biosignal Processing

Dominic Telaar, Michael Wand, Dirk Gehrig, Felix Putze, Christoph Amma, Dominic Heger, Ngoc Thang Vu, Mark Erhardt, Tim Schlippe, Matthias Janke, Christian Herff, Tanja Schultz

KIT, Germany

We introduce BioKIT, a new Hidden Markov Model based toolkit to preprocess, model and interpret biosignals such as speech, motion, muscle and brain activities. The focus of this toolkit is to enable researchers from various communities to pursue their experiments and integrate real-time biosignal interpretation into their applications. BioKIT boosts a flexible two-layer structure with a modular C++ core that interfaces with a Python scripting layer, to facilitate development of new applications. BioKIT employs sequence-level parallelization and memory sharing across threads. Additionally, a fully integrated error blaming component facilitates in-depth analysis. A generic terminology keeps the barrier to entry for researchers from multiple fields to a minimum. We describe our onlinecapable dynamic decoder and report on initial experiments on three different tasks. The presented speech recognition experiments employ Kaldi [1] trained deep neural networks with the results set in relation to the real time factor needed to obtain them.


  1. D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, and P. Schwarz, “The Kaldi Speech Recognition Toolkit,” in IEEE Automatic Speech Recognition and Understanding (ASRU) Workshop, 2011, pp. 1–4.

Full Paper

Bibliographic reference.  Telaar, Dominic / Wand, Michael / Gehrig, Dirk / Putze, Felix / Amma, Christoph / Heger, Dominic / Vu, Ngoc Thang / Erhardt, Mark / Schlippe, Tim / Janke, Matthias / Herff, Christian / Schultz, Tanja (2014): "BioKIT — real-time decoder for biosignal processing", In INTERSPEECH-2014, 2650-2654.