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arXiv:1604.08242cs.CL

The IBM 2016 English Conversational Telephone Speech Recognition System

George Saon, Tom Sercu, Steven Rennie, Hong-Kwang J. Kuo

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Abstract

We describe a collection of acoustic and language modeling techniques that lowered the word error rate of our English conversational telephone LVCSR system to a record 6.6% on the Switchboard subset of the Hub5 2000 evaluation testset. On the acoustic side, we use a score fusion of three strong models: recurrent nets with maxout activations, very deep convolutional nets with 3x3 kernels, and bidirectional long short-term memory nets which operate on FMLLR and i-vector features. On the language modeling side, we use an updated model "M" and hierarchical neural network LMs.

arXiv comments
Submitted to Interspeech 2016
Journal reference
DOI
Subjects
cs.CLComputation and Language

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  1. Say_what: Using speech-to-text to fully check out during conference calls 1 comment

    • * I should clarify as well, for your first question. State of the art is generally either IBM's 2016 English Conversational Telephone Speech Recognition System [0], or Baidu's DeepSpeech2 [1]. IBM's is a little more complex, but I'd actually argue Baidu is more promising due to…

      skoocdaView on HNParent context

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