BROADBAND BEAMPATTERN FOR MULTI-CHANNEL SPEECH ACQUISITION AND DISTANT SPEECH RECOGNITION
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State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
\begin{abstract} In this paper, we address an adaptive beamforming application in realistic acoustic conditions. After the position of a speaker is estimated by a speaker tracking system, we construct a subband-domain beamformer in \emph{generalized sidelo ...
Close-talk headset microphones have been traditionally used for speech acquisition in a number of applications, as they naturally provide a higher signal-to-noise ratio -needed for recognition tasks- than single distant microphones. However, in multi-party ...
Close-talk headset microphones have been traditionally used for speech acquisition in a number of applications, as they naturally provide a higher signal-to-noise ratio -needed for recognition tasks- than single distant microphones. However, in multi-party ...
State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
State-of-the-art automatic speech recognition (ASR) techniques are typically based on hidden Markov models (HMMs) for the modeling of temporal sequences of feature vectors extracted from the speech signal. At the level of each HMM state, Gaussian mixture m ...
