Robust Source-Filter Separation of Speech Signal in the Phase Domain

Erfan Loweimi, Jon Barker, Oscar Saz Torralba, Thomas Hain

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

Abstract

In earlier work we proposed a framework for speech source-filter separation that employs phase-based signal processing. This paper presents a further theoretical investigation of the model and optimisations that make the filter and source representations less sensitive to the effects of noise and better matched to downstream processing. To this end, first, in computing the Hilbert transform, the log function is replaced by the generalised logarithmic function. This introduces a tuning parameter that adjusts both the dynamic range and distribution of the phase-based representation. Second, when computing the group delay, a more robust estimate for the derivative is formed by applying a regression filter instead of using sample differences. The effectiveness of these modifications is evaluated in clean and noisy conditions by considering the accuracy of the fundamental frequency extracted from the estimated source, and the performance of speech recognition features extracted from the estimated filter. In particular, the proposed filter-based front-end reduces Aurora-2 WERs by 6.3% (average 0–20 dB) compared with previously reported results. Furthermore, when tested in a LVCSR task (Aurora-4) the new features resulted in 5.8% absolute WER reduction compared to MFCCs without performance loss in the clean/matched condition.
Original languageEnglish
Title of host publicationProc. Interspeech 2017
Place of PublicationStockholm, Sweden
PublisherISCA
Pages414-418
Number of pages5
DOIs
Publication statusPublished - 24 Aug 2017
EventInterspeech 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017
http://www.interspeech2017.org/

Publication series

Name
PublisherISCA
ISSN (Electronic)1990-9772

Conference

ConferenceInterspeech 2017
CountrySweden
CityStockholm
Period20/08/1724/08/17
Internet address

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