The Marchex 2018 English Conversational Telephone Speech Recognition System

11/05/2018
by   Seongjun Hahm, et al.
0

In this paper, we describe recent improvements to the production Marchex speech recognition system for our spontaneous customer-to-business telephone conversations. We outline our semi-supervised lattice-free maximum mutual information (LF-MMI) training process which can supervise over full lattices from unlabeled audio. We also elaborate on production-scale text selection techniques for constructing very large conversational language models (LMs). On Marchex English (ME), a modern evaluation set of conversational North American English, for acoustic modeling we report a 3.3 reduction in absolute word error rate (WER). For language modeling, we observe a separate 1.3 respectively over the performance of the 2017 production system.

READ FULL TEXT
research
03/06/2017

English Conversational Telephone Speech Recognition by Humans and Machines

One of the most difficult speech recognition tasks is accurate recogniti...
research
05/26/2017

Semi-Supervised Model Training for Unbounded Conversational Speech Recognition

For conversational large-vocabulary continuous speech recognition (LVCSR...
research
05/03/2016

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

We present a new tool for training neural network language models (NNLMs...
research
04/27/2016

The IBM 2016 English Conversational Telephone Speech Recognition System

We describe a collection of acoustic and language modeling techniques th...
research
10/17/2016

Achieving Human Parity in Conversational Speech Recognition

Conversational speech recognition has served as a flagship speech recogn...
research
05/30/2019

Lattice-based lightly-supervised acoustic model training

In the broadcast domain there is an abundance of related text data and p...
research
12/29/2017

The CAPIO 2017 Conversational Speech Recognition System

In this paper we show how we have achieved the state-of-the-art performa...

Please sign up or login with your details

Forgot password? Click here to reset