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 di(More)
The combination of a model of auditory perception (PEMO) as feature extractor and of a Locally Recurrent Neural Network (LRNN) as classi er yields promising ASR results in noise. Our study focuses on the interplay between both techniques and their ability to complement each other in the task of robust speech recognition. We performed recognition experiments(More)
Conventional speaker identification systems are already field-proven with respect to recognition accuracy. Since any biometric identification requires exhaustive 1 : N comparisons for identifying a biometric probe, comparison time frequently dominates the overall computational workload, preventing the system from being executed in real-time. In this paper(More)
In this paper we summarize the experiences gained from a field trial of a speaker verification system. In the test implementation access to two rooms at the University of Frankfurt had been controlled by a speaker verification system. The paper is organized as follows: Firstly, we will describe the system concepts and implementation issues. Secondly,(More)
It is generally conceded that duration variability has huge effects on the biometric performance of speaker recognition systems. State-of-the-art approaches, which employ ivector representations, apply adaptive symmetric (AS) scorenormalizations to improve the performance of the underlying system by using specific statistics on reference and probe templates(More)
Biometric speaker verification deals with the recognition of voice and speech features to reliably identify a user and to offer him a comfortable alternative to knowledge-based authentication methods like passwords. As more and more personal data is saved on smartphones and other mobile devices, their security is in the focus of recent applications.(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)