Speech Acoustic Modelling From Raw Phase Spectrum

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

Abstract

Magnitude spectrum-based features are the most widely employed front-ends for acoustic modelling in automatic speech recognition (ASR) systems. In this paper, we investigate the possibility and efficacy of acoustic modelling using the raw short-time phase spectrum. In particular, we study the usefulness of the raw wrapped, unwrapped and minimum-phase phase spectra as well as the phase of the source and filter components for acoustic modelling. Furthermore, we explore the effectiveness of simultaneous deployment of the vocal tract and excitation components of the raw phase spectrum using multi-head CNNs and investigate multiple information fusion schemes. This paves the way for developing an effective phase-based multi-stream information processing systems for speech recognition. The performance, even for wrapped phase with a noise-like shape, is comparable to or better than the magnitude-based classic features, and up to 4.8% WER has been achieved in the WSJ (Eval-92) task.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
Publication statusAccepted/In press - 30 Jan 2021
Event46th IEEE International Conference on Acoustics, Speech and Signal Processing - Toronto, Canada
Duration: 6 Jun 202111 Jun 2021
https://2021.ieeeicassp.org/

Conference

Conference46th IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP 2021
CountryCanada
CityToronto
Period6/06/2111/06/21
Internet address

Keywords

  • Raw phase spectrum
  • phase-based source-filter separation
  • multi-head CNNs
  • acoustic modelling
  • ASR

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