Efficient Pitch-based Estimation of VTLNWarp Factors

Abstract

To reduce inter-speaker variability, vocal tract length normalization (VTLN) is commonly used to transform acoustic features for automatic speech recognition (ASR). The warp factors used in this process are usually derived by maximum likelihood (ML) estimation, involving an exhaustive search over possible values. We describe an alternative approach: exploit the correlation between a speaker's average pitch and vocal tract length, and model the probability distribution of warp factors conditioned on pitch observations. This can be used directly for warp factor estimation, or as a smoothing prior in combination with ML estimates. Pitch-based warp factor estimation for VTLN is effective and requires relatively little memory and computation. Such an approach is well-suited for environments with constrained resources, or where pitch is already being computed for other purposes.