Geoffrey Zweig · PaperMetrix

The Microsoft 2016 Conversational Speech Recognition System

2016 · arXiv (Cornell University)

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art …

Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces

2020 · arXiv (Cornell University)

In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state-of-the-art results. We then show that using wordpieces as modeling units combined with …

Improving RNN Transducer Based ASR with Auxiliary Tasks

2021

End-to-end automatic speech recognition (ASR) models with a single neural network have recently demonstrated state-of-the-art results compared to conventional hybrid speech recognizers. Specifically, recurrent neural network transducer (RNN-T) has shown competitive ASR performance on various …

Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion

2015 · arXiv (Cornell University)

Sequence-to-sequence translation methods based on generation with a side-conditioned language model have recently shown promising results in several tasks. In machine translation, models conditioned on source side words have been used to produce target-language text, …

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

2017

End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors.

From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition

2019 · 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)