Edinburgh Research Explorer

Acoustic model adaptation from raw waveforms with Sincnet

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

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE Automatic Speech Recognition and Understanding Workshop
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
Publication statusAccepted/In press - 13 Sep 2019
EventIEEE Automatic Speech Recognition and Understanding Workshop 2019 - Sentosa, Singapore
Duration: 14 Dec 201918 Dec 2019
http://asru2019.org/wp/

Conference

ConferenceIEEE Automatic Speech Recognition and Understanding Workshop 2019
Abbreviated titleASRU 2019
CountrySingapore
CitySentosa
Period14/12/1918/12/19
Internet address

Abstract

Raw waveform acoustic modelling has recently gained interest due to neural networks’ ability to learn feature extraction, and the potential for finding better representations for a given scenario than hand-crafted features. SincNet has been proposed to reduce the number of parameters required in rawwaveform modelling, by restricting the filter functions, rather than having to learn every tap of each filter. We study the adaptation of the SincNet filter parameters from adults’ to children’s speech, and show that the parameterisation of the SincNet layer is well suited for adaptation in practice: we can efficiently adapt with a very small number of parameters, producing error rates comparable to techniques using orders of magnitude more parameters.

    Research areas

  • Acoustic model adaptation, children’s speech, raw waveform, SincNet

Event

IEEE Automatic Speech Recognition and Understanding Workshop 2019

14/12/1918/12/19

Sentosa, Singapore

Event: Conference

ID: 118997343