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Sequence-to-Sequence Models for Punctuated Transcription Combing Lexical and Acoustic Features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationThe 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5700-5704
Number of pages5
ISBN (Print)978-1-5090-4117-6
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech and Signal Processing - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017
http://www.ieee-icassp2017.org/

Conference

Conference42nd IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17
Internet address

Abstract

In this paper we present an extension of our previously described neural machine translation based system for punctuated transcription. This extension allows the system to map from per frame acoustic features to word level representations by replacing the traditional encoder in the encoder-decoder architecture with a hierarchical encoder. Furthermore, we show that a system combining lexical and acoustic features significantly outperforms systems using only a single source
of features on all measured punctuation marks. The combination of lexical and acoustic features achieves a significant improvement in F-Measure of 1.5 absolute over the purely lexical neural machine translation based system.

Event

42nd IEEE International Conference on Acoustics, Speech and Signal Processing

5/03/179/03/17

New Orleans, United States

Event: Conference

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