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Speech recognition systems perform poorly in the presence of corrupting noise. Missing feature methods attempt to compensate for the noise by removing noise corrupted components of spectrographic representations of noisy speech and performing recognition with the remaining reliable components. Conventional classifier-compensation methods modify the(More)
• Where represents the power spectrum of the degraded speech, is the power spectrum of the clean speech, is the transfer function of the linear filter, and is the power spectrum of the additive noise.) (|) (|) () Z(2 ω ω ω ω N H X + =) Z(ω) X(ω) (ω H) (ω N • In the log-Spectral domain this relation can be expressed as:) 1 log(q x n e q x z − − + + + = of in(More)
Sphinx-4 is a flexible, modular and pluggable framework to help foster new innovations in the core research of hidden Markov model (HMM) recognition systems. The design of Sphinx-4 is based on patterns that have emerged from the design of past systems as well as new requirements based on areas that researchers currently want to explore. To exercise this(More)
In this paper we describe a model developed for the analysis of acoustic spectra. Unlike decom-positions techniques that can result in difficult to interpret results this model explicitly models spectra as distributions and extracts sets of additive and semantically useful components that facilitate a variety of applications ranging from source separation,(More)
In this paper we describe a technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality. Our approach employs a probabilistic latent variable model with sparsity constraints. We demonstrate its utility by performing feature extraction in a variety of domains ranging from(More)
We present an algorithm for dereverberation of speech signals for automatic speech recognition (ASR) applications. Often ASR systems are presented with speech that has been recorded in environments that include noise and reverberation. The performance of ASR systems degrades with increasing levels of noise and reverberation. While many algorithms have been(More)
We present a technique for denoising speech using nonnegative matrix factorization (NMF) in combination with statistical speech and noise models. We compare our new technique to standard NMF and to a state-of-the-art Wiener filter implementation and show improvements in speech quality across a range of interfering noise types.
In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenarios. One being the case where all sound types in the mixture(More)
This paper investigates the use of higher-order autoregressive vector predictors for tracking the noise in noisy speech signals. The autoregressive predictors form the state equation of a linear dynamical system that models the spectral dynamics of the noise process. Experiments show that the use of such models to track noise can lead to large gains in(More)
This paper proposes a computationally efficient algorithm for estimating the non-negative weights of linear combinations of the atoms of large-scale audio dictionaries, so that the generalized Kullback-Leibler divergence between an audio observation and the model is minimized. This linear model has been found useful in many audio signal processing tasks,(More)