861 citations · 1k across the 13 of their papers we have counts for
6 papers · 1 filter
Adversarial Training for Multilingual Acoustic Modeling
Ke Hu, Hasim Sak, Hank Liao
Multilingual training has been shown to improve acoustic modeling performance by sharing and transferring knowledge in modeling different languages. Knowledge sharing is usually ac…
Exploring Architectures, Data and Units For Streaming End-to-End Speech Recognition with RNN-Transducer
Kanishka Rao, Haşim Sak, Rohit Prabhavalkar
We investigate training end-to-end speech recognition models with the recurrent neural network transducer (RNN-T): a streaming, all-neural, sequence-to-sequence architecture which…
Speech recognition for medical conversations
Chung-Cheng Chiu, Anshuman Tripathi, Katherine Chou +11
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ($1…
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
Hagen Soltau, Hank Liao, Hasim Sak
We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We…
Personalized Speech recognition on mobile devices
Ian McGraw, Rohit Prabhavalkar, Raziel Alvarez +8
We describe a large vocabulary speech recognition system that is accurate, has low latency, and yet has a small enough memory and computational footprint to run faster than real-ti…
Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition
Haşim Sak, Andrew Senior, Kanishka Rao +1
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech re…