Time-Frequency Analysis has previously been successfully applied to characterize and quantify a variety of acoustic signals, including marine mammal sounds. In this research, Time-Frequency analysis is applied to human speech signals in an effort to reveal signal structure salient to the biometric speaker verification challenge. Prior approaches to speaker verification have relied upon signal processing analysis such as linear prediction or weighted Cepstrum spectral representations of segments of speech and classification techniques based on stochastic pattern matching. The authors believe that the classification of identity of a speaker based on time-frequency representation of short time events occurring in speech could have substantial advantages. Using these ideas, a speaker verification algorithm was developed1 and has been refined over the past several years. In this presentation, the authors describe the testing of the algorithm using a large speech database, the results obtained, and recommendations for further improvements.
Biometrics has become an increasingly important part of the overall set of tools for securing a wide range of facilities, areas, information, and environments. At the core of any biometric verification/identification technique lies the development of the algorithm itself. Much research has been performed in this area to varying degrees of success, and it is well recognized within the biometrics community that substantial room for improvement exists. The focus of this paper is to describe ongoing biometrics algorithm development efforts by the authors. An overview of the data collection, algorithm development, and testing efforts is described. The focus of the research is to develop core algorithmic concepts that serve as the basis for robust techniques in both the face and speech modalities. A broad overview of the methodology is provided with some sample results.
The end goal is to have a robust, modular set of tools which can balance complexity and need for accuracy and robustness for a wide variety of applications.