4 citations · 7 across the 5 of their papers we have counts for
6 papers · 1 filter
Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition
Christian Huber, Juan Hussain, Sebastian Stüker +1
Neural sequence-to-sequence systems deliver state-of-the-art performance for automatic speech recognition (ASR). When using appropriate modeling units, e.g., byte-pair encoded char…
Neural Language Codes for Multilingual Acoustic Models
Markus Müller, Sebastian Stüker, Alex Waibel
Multilingual Speech Recognition is one of the most costly AI problems, because each language (7,000+) and even different accents require their own acoustic models to obtain best re…
Self-Attentional Acoustic Models
Matthias Sperber, Jan Niehues, Graham Neubig +2
Self-attention is a method of encoding sequences of vectors by relating these vectors to each-other based on pairwise similarities. These models have recently shown promising resul…
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
Odette Scharenborg, Laurent Besacier, Alan Black +16
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and word…
Comparison of Decoding Strategies for CTC Acoustic Models
Thomas Zenkel, Ramon Sanabria, Florian Metze +4
Connectionist Temporal Classification has recently attracted a lot of interest as it offers an elegant approach to building acoustic models (AMs) for speech recognition. The CTC lo…
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Robin Ruede, Markus Müller, Sebastian Stüker +1
Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is…