Neural models for extracting speaker characteristics in speech modelization systems
Thierry Artières, Patrick Gallinari · 1993
We analyze in this paper inherent limitations of prediction systems for speaker identification. We introduce different approaches for enhancing these systems and present results of tests on TIMIT with neural net predictive systems. Keywords : speaker recognition, predictive neural networks, sequence classification. 1. INTRODUCTION Over the last 15 years, several approaches have been tested for speaker recognition problems. Many of them use static characteristics of speaker utterances computed from long term statistics or short term spectra. More recently dynamical approaches have been used to modelize more accurately speaker characteristics. Most of them have been inspired from speech recognition techniques. Several systems have thus been proposed based on Hidden Markov Models (HMM) [1], Vectorial Autoregressive Models (VAM) [2,3] or Neural Networks (NN) [4]. We will focus here on free text speaker identification by neural networks. Whereas long term statistics may contain enough infor...