Bengt J. Borgstrom

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This paper proposes a new statistical model-based likelihood ratio test (LRT) VAD to obtain reliable speech / non-speech decisions. In the proposed method, the likelihood ratio (LR) is calculated differently for voiced frames, as opposed to unvoiced frames: only DFT bins containing harmonic spectral peaks are selected for LR computation. To evaluate the new(More)
This paper presents a framework for efficient HMM-based estimation of unreliable spectrographic speech data. It discusses the role of hidden Markov models (HMMs) during minimum mean-square error (MMSE) spectral reconstruction. We develop novel HMM-based reconstruction algorithms which exploit intra-channel (across-time) correlation and/or inter-channel(More)
Sodium dodecyl sulfate (SDS) binds to pancreatic lipase in a cooperative manner up to about 200 moles/mole of protein. This binding in a rapid and irreversible inactivation of lipase. Bile salts under certain conditions prevent SDS inactivation of lipase when both detergents are present together. Under these conditions bile salts also prevent the binding of(More)
In this letter, we propose a novel algorithm for reconstructing unreliable spectrographic data, a method applicable to missing feature-based automatic speech recognition (ASR). We provide quantitative analysis illustrating the high compressibility of spectrographic speech data. The existence of sparse representations for spectrographic data motivates the(More)
We propose a novel packetization and variable bitrate compression scheme for DSR source coding, based on the Group of Pictures concept from video coding. The proposed algorithm simultaneously packetizes and further compresses source coded features using the high interframe correlation of speech, and is compatible with a variety of VQ-based DSR source(More)
In this letter, we present a statistical approach to Mel-domain mask estimation for missing feature (MF)-based automatic speech recognition (ASR). Mel-domain time-frequency masks are of interest, since MF systems have been shown successful in that domain. Time- and channel-specific reliability measures are derived as posterior probabilities of active speech(More)
This paper presents a framework for fully Bayesian speaker comparison of i-vectors. By generalizing the train/test paradigm, we derive an analytic expression for the speaker comparison log-likelihood ratio (LLR), as well as solutions for model training and Bayesian scoring. This framework is useful for enrollment sets of any size. For the specific case of(More)