Volker Leutnant

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In this paper we present an analytic derivation of the moments of the phase factor between clean speech and noise cepstral or log-mel-spectral feature vectors. The development shows, among others, that the probability density of the phase factor is of sub-Gaussian nature and that it is independent of the noise type and the signal-to-noise ratio, however(More)
In this paper we present a system for identifying and localizing speakers using distant microphone arrays and a steerable pan-tilt-zoom camera. The scenario at hand assumes audio streams to be processed in real-time to get the diarization information " who spokes when and where " with only short delays. Our new idea is to fuse the acoustical and visual(More)
The Amigo Context Management Service (CMS) provides an open infrastructure for the exchange of contextual information between context sources and context clients. Whereas context sources supply context information, retrieved from sensors or services within the networked home environment, context clients utilize those information to become context-aware. An(More)
In this contribution we investigate the effectiveness of BAYESIAN feature enhancement (BFE) on a medium-sized recognition task containing real-world recordings of noisy re-verberant speech. BFE employs a very coarse model of the acoustic impulse response (AIR) from the source to the microphone , which has been shown to be effective if the speech to be(More)
In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction(More)
In this paper we consider the combination of hidden Markov models based on Gaussian mixture densities (GMM-HMM) and linear dynamic models (LDM) as the acoustic model for automatic speech recognition systems. In doing so, the individual strengths of both models, i.e. the modelling of long-term temporal dependencies by the GMM-HMM and the direct modelling of(More)