Jan Chorowski

On Multilingual Training of Neural Dependency Parsers

2017 · arXiv (Cornell University)

We show that a recently proposed neural dependency parser can be improved by joint training on multiple languages from the same family. The parser is implemented as a deep neural network whose only input is …

End-to-end attention-based large vocabulary speech recognition

2016

Many state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) Systems are hybrids of neural networks and Hidden Markov Models (HMMs). Recently, more direct end-to-end methods have been investigated, in which neural architectures were trained to model …

Towards Better Decoding and Language Model Integration in Sequence to Sequence Models

2017

The recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion.In this contribution, we analyse an …

Sequence-to-Sequence Models Can Directly Translate Foreign Speech

2017

We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another.The model does not explicitly transcribe the speech into text in the source language, nor does …

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

2019 · arXiv (Cornell University)

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models are composed of modular building blocks that are flexible and easily extensible, …

Attention-Based Models for Speech Recognition

2015 · arXiv (Cornell University)

Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation, handwriting synthesis and image caption gen- eration. We extend …