Speaker Modeling Using Emotional Speech for More Robust Speaker Identification

Abstract

Automatic identity recognition in fast, reliable and non-intrusive way is one of the most challenging topics in digital world of today. A possible approach to identity recognition is the identification by voice. Characteristics of speech relevant for automatic speaker recognition can be affected by external factors such as noise and channel distortions, but also by speaker-specific conditions—emotional or health states. The improvement of a speaker recognition system by different model training strategies are addressed in this paper in order to obtain the best performance of the system with only a limited amount of neutral and emotional speech data. The models adopted are a Gaussian Mixture Model and i-vectors whose inputs are Mel Frequency Cepstral Coefficients, and the experiments have been conducted on the Russian Language Affective speech database. The results show that the appropriate use of emotional speech in speaker model training improves the robustness of a speaker recognition system – both when tested on neutral and emotional speech.