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Model-based speech enhancement algorithms that employ trained models, such as codebooks, hidden Markov models, Gaussian mixture models, etc., containing representations of speech such as linear predictive coefficients, mel-frequency cepstrum coefficients, etc., have been found to be successful in enhancing noisy speech corrupted by nonstationary noise. However, these models are typically trained on...
Single-microphone speech enhancement algorithms that employ trained codebooks of parametric representations of speech spectra have been shown to be successful in the suppression of non-stationary noise, e.g., in mobile phones. In this paper, we introduce the concept of a context-dependent codebook, and look at two aspects of context: dependency on the particular speaker using the mobile device, and...
Speech enhancement techniques that employ trained codebooks of speech and noise linear predictive coefficients have been previously shown to provide good performance in non-stationary noise environments. The speech codebooks are typically trained using data from a large number of speakers. For noise reduction systems in personal devices such as mobile phones, the use of speaker dependent codebooks...
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