Current state-of-the-art systems for automatic phonetic transcription (APT) are mostly phone recognizers based on Hidden Markov models (HMMs). We present a different approach for ...
Christina Leitner, Martin Schickbichler, Stefan Pe...
We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum en...
We describe an acoustic chord transcription system that uses symbolic data to train hidden Markov models and gives best-of-class frame-level recognition results. We avoid the extre...
In this paper we present a new architecture for face recognition with a single reference image, which completely separates the training process from the recognition process. In th...
A new approach to the recognition of temporal behaviors and activities is presented. The fundamental idea, inspired by work in speech recognition, is to divide the inference probl...