Herbert Reininger

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This paper investigates two methods to define a distance measure between any pair of Hidden Markov Models (HMM). The first one is the geometricaly motivated euclidean distance which solely incorporates the feature probabilities. The second measure is the Kulback-Liebler distance which is based on the discriminating power of the probability measure on the(More)
In this paper we present a robust speaker independent speech recognition system consisting of a feature extraction based on a model of the auditory periphery, and a Locally Recurrent Neural Network for scoring of the derived feature vectors. A number of recognition experiments were carried out to investigate the robustness of this combination against(More)
Recurrent Neural Networks (RNN) provide a solution for low cost Speech Recognition Systems (SRS) in mass products or in products with energetic constraints if their inherent parallelism could be exploited in a hardware realization. Actually, the computational complexity of SRS based on Fully Recurrent Neural Networks (FRNN), e.g. the large number of(More)
Conventional speaker identification systems are already field-provenw ith respect to recognition accuracy. Since anyb iometric identification requires exhaustive 1:Ncomparisons for identifying abiometric probe, comparison time frequently dominates the overall computational workload, preventing the system from being executed in real-time. In this paper we(More)
In speaker verification, score normalization methods are acommon practice to gain better performance and robustness. One kind of score normalization is cohort normalization, which uses information about the score behaviour of known impostors. During enrolment, impostor verifications are simulated to get aspeaker-specific set of the most competitive(More)