In-document adaptation for a human guided automatic transcription service

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • University of Helsinki

Abstract

In this work, the task is to assist human transcribers to produce, for example, interview or parliament speech transcriptions. The system will perform in-document adaptation based on a small amount of manually corrected automatic speech recognition results. The corrected segments of the spoken document are used to adapt the speech recognizer’s acoustic and language model. The updated models are used in second-pass recognition to produce a more accurate automatic transcription for the remaining uncorrected parts of the spoken document. In this work we evaluate two common adaptation methods for speech data in settings that represent typical transcription tasks. For adapting the acoustic model we use the Maximum A Posteriori adaptation method. For adapting the language model we use linear interpolation. We compare results of supervised adaptation to unsupervised adaptation, and evaluate the total benefit of using human corrected segments for in-document adaptation for typical transcription tasks.

Details

Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Speech and Computer, SPECOM 2016
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Speech and Computer - Budapest, Hungary
Duration: 23 Aug 201627 Aug 2016
Conference number: 18
http://www.specom2016.hte.hu/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9811
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

ConferenceInternational Conference on Speech and Computer
Abbreviated titleSPECOM
CountryHungary
CityBudapest
Period23/08/201627/08/2016
Internet address

    Research areas

  • Acoustic model adaptation, Automatic speech recognition, Human guided speech recognition, Language model adaptation

ID: 9506026