Andreas Stolcke

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 …

ASR-aware end-to-end neural diarization

2022

We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model. Two categories of features are explored: features derived directly from ASR …

Recurrent neural network and LSTM models for lexical utterance classification

2015

Utterance classification is a critical pre-processing step for many speech understanding and dialog systems. In multi-user settings, one needs to first identify if an utterance is even directed at the system, followed by another level …