Deep Learning for Speech Recognition
Speech technology makes it ever faster from research conferences to the consumer market. Deep learning accelerated this trend in 2010-2013. This talk will depict advances in speech technology, mainly due to deep neural net models, but not only. We’ll go through architectures in use today (DNN acoustic models, but also CNNs and more recently long-short-term memories). I’ll draw the connection to business issues such as the need for privacy-preserving (eg embedded) technology, or opportunities for small teams who don’t command huge computing clusters and masses of data. Finally, I’ll give an outlook on future directions: end-to-end speech recognition, the integration of spoken language understanding.
Sébastien Bratières has spent 15 years in the speech and language industry in different European ventures, starting from the EU branch of Tellme Networks (now Microsoft) to startups in speech recognition and virtual conversational agents. Today, Sébastien is engaged in a PhD in statistical machine learning with Zoubin Ghahramani at the University of Cambridge, UK, and consults for dawin gmbh, a German SME producing custom speech solutions for industry use.
Sébastien graduated with master’s degrees from Ecole Centrale Paris, France, in engineering, and from the University of Cambridge in speech and language processing.