ESSV Konferenz Elektronische Sprachsignalverarbeitung

Title: Unsupervised Neural-Network Based Vocal Tract Length Normalization

Authors: Philip Harding, Matthew Gibson

Abstract:

In this paper an efficient, unsupervised, method of warp factor estimation for vocal tract length normalisation (VTLN) is proposed. VTLN is a method of feature-based speaker normalisation where the frequency spectrum is warped to produce speaker-independent spectral features that are invariant to vocal tract length. The degree to which the spectrum is warped is determined by the warping factor, and it is the estimation of this warping factor that is the focus of this paper. The warping factor is typically obtained using a maximum likelihood-based technique that requires a state alignment for each utterance and a GMM acoustic model trained on warped features. The warp factor is typically quantised, with one of N warp factors selected for each utterance. The proposed method of warp factor estimation makes use of a small neural network, trained on un-warped features, to directly estimate the quantised warp factor. Experimental results are presented where, unlike previously published methods of unsupervised warp factor estimation [1, 2], the proposed method is shown to give equivalent performance to the typical supervised GMM-based method in terms of ASR accuracy at a significantly lower computational cost.


Year: 2018
In session: Poster
Pages: 70 to 76