22 citations · 26 across the 2 of their papers we have counts for
5 papers
Direct Acoustics-to-Word Models for English Conversational Speech Recognition
Kartik Audhkhasi, Bhuvana Ramabhadran, George Saon +2
Recent work on end-to-end automatic speech recognition (ASR) has shown that the connectionist temporal classification (CTC) loss can be used to convert acoustics to phone or charac…
English Conversational Telephone Speech Recognition by Humans and Machines
George Saon, Gakuto Kurata, Tom Sercu +9
One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major sp…
Training variance and performance evaluation of neural networks in speech
Ewout van den Berg, Bhuvana Ramabhadran, Michael Picheny
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well…
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani +8
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and fra…
The IBM 2015 English Conversational Telephone Speech Recognition System
George Saon, Hong-Kwang J. Kuo, Steven Rennie +1
We describe the latest improvements to the IBM English conversational telephone speech recognition system. Some of the techniques that were found beneficial are: maxout networks wi…