Speaker verification using heterogeneous neural network architecture with linear correlation speech activity detection

Abstract

AbstractThis paper presents a multi‐level speaker verification system that uses 64 discrete Fourier transform spectrum components as input feature vectors. A speech activity detection technique is used as a pre‐processing stage to identify vowel phoneme boundaries within a speech sample. A modified self‐organising map (SOM) is then used to filter the speech data by using cluster information extracted from three vowels for a claimed speaker. This SOM filtering stage also provides coarse speaker verification. Finally, a second speaker verification level of three multi‐layer perceptron networks classifies the filtered frames provided by the SOMs. These multi‐layer perceptrons work as fine‐grained vowel‐based speaker verifiers. The proposed verification algorithm shows a performance of 94.54% when evaluated using 50 speakers from the Centre for Spoken Language Understanding speaker verification database. In addition, it is shown that the novel discrete Fourier transform spectrum‐based linear correlation pre‐processing technique, presented here, provides the system with greater robustness against changes in speech volume levels when compared with an equivalent energy frame analysis.