Improving short-duration speaker recognition by joint Bark-wavelet acoustic feature coupling and triplet dual-attention mechanism network

Abstract

Speaker recognition methods are negatively affected by the input short duration audio signal. In this paper, we tackle the problem of speaker recognition from shorter speech data by coupling proposed two acoustic features: Bark-scaled Gauss Filter Cepstral Coefficients (BGCC) and Perceptual Wavelet Packet Entropy (PWPE). Our assumption is based on the observation that BGCC and PWPE capture sufficient information on various aspects of speech that can be used to discriminate speaker, viz., speech perception and high time-frequency information representation, etc., for enhancing characteristic diversity. A triplet dual attention mechanism (Triplet-DAM) is used to couple these two features in a creative manner. The coupling method means that the feature of limited short utterance can be reused, through the dual attention mechanism, more discriminative features are enhanced for limited feature, thus improving speaker recognition performance in short duration audio signals. Extensive analysis on a variety of datasets, which speech samples of different types, diverse lengths, etc., demonstrate the superiority of the proposed feature engineering and method over existing acoustic feature extraction and speaker recognition algorithms, including those based on MFCCs, LPCCs features, and GMM-UBM, iVector-PLDA, ResCNN-triplet, respectively. The experimental results demonstrate the proposed method achieves notable improvement with the existing approach for short duration speaker recognition.