Abstract
This paper proposes novel features for automated language and dialect identification that aim to improve discriminative power by ensuring that each element of the feature vector has a normalised contribution to inter-class variance. The method firstly computes inter- and intra-class frequency variance statistics and then distributes the overall spectral variance across spectral regions which are sized to contain near-equal-variance difference. Spectral features are average pooled within regions to obtain variance normalised features (VNFs). The proposed VNFs are low complexity drop-in replacements for MFCC, SDC, PLP or other input features used for speech-related tasks. In this paper, they are evaluated in three types of system, against MFCCs, for two data-constrained language and dialect identification tasks. VNFs demonstrate good results, comfortably outperforming MFCCs at most dimension sizes, and yielding particularly good performance for the most challenging data-constrained 3s utterance length in the LID task.
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