Florian Metze: End-to-end learning for language universal speech recognition

One of the great successes of end-to-end learning strategies such as Connectionist Temporal Classification in automatic speech recognition is the ability to train very powerful models that map directly from features to characters or context independent phones. Traditional hybrid models, or even GMMs usually require context dependent states and a Hidden Markov Model in order to achieve good performance. By contrast, with CTC, it thus becomes possible to train a multi-lingual RNN that can directly predict phones in multiple languages (multi-task training), language independent articulatory features, and language universal phones, allowing for the recognition of speech in languages for which no acoustic training data is available.