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Robust speech Recognition

Acoustical Pre Processing for Robust Speech Recognition

Acoustical Pre Processing for Robust Speech Recognition

... ACOUSTICAL PRE PROCESSING FOR ROBUST SPEECH RECOGNITION ACOUSTICAL PRE PROCESSING FOR ROBUST SPEECH RECOGNITION Richard M Stern and Alejandro Acero 1 School of Computer Science Carnegie Mellon Univers[.] ...

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Efficient Cepstral Normalization for Robust Speech Recognition

Efficient Cepstral Normalization for Robust Speech Recognition

... EFFICIENT CEPSTRAL NORMALIZATION FOR ROBUST SPEECH RECOGNITION EFFICIENT CEPSTRAL NORMALIZATION FOR ROBUST SPEECH R E C O G N I T I O N Fu Hua Liu, Richard M Stern, Xuedong Huang, Alejandro Acero Depa[.] ...

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Histogram Equalization to Model Adaptation for Robust Speech Recognition

Histogram Equalization to Model Adaptation for Robust Speech Recognition

... clean speech models and compensated features in the decoding process of ...clean speech models can be fully adapted into acoustically matched speech models as far as the amount of adaptation data is ...

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Microphone Arrays and Neural Networks for Robust Speech Recognition

Microphone Arrays and Neural Networks for Robust Speech Recognition

... vIicrophone Arrays and Neural Networks for Robust Speech Recognition vIicrophone Arrays and N e u r a l N e t w o r k s for R o b u s t S p e e c h R e c o g n i t i o n C Che +, Q Lin +, J Pearson*,[.] ...

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Noise Robust Speech Recognition Applied to Voice-Driven Wheelchair

Noise Robust Speech Recognition Applied to Voice-Driven Wheelchair

... sufficient recognition accuracy, even in the presence of surrounding ...the speech recognition accuracy drastically degrades when the microphone is placed far from the ...noise robust ...

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A Review on Neural Network based Noise Robust Speech Recognition Methods

A Review on Neural Network based Noise Robust Speech Recognition Methods

... enhancing speech by means of a deep neural network based ...noisy speech signals to clean speech ...where speech data is intensely contaminated by noisy ...of speech frame for the ...

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Role of Spectral Peaks in Autocoorelation Domain for Robust Speech Recognition

Role of Spectral Peaks in Autocoorelation Domain for Robust Speech Recognition

... for robust speech ...filtered speech signals in the autocorrelation ...for robust speech extraction is proposed in the autocorrelation ...the speech signal corrupted by an ...

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Spectral Estimation for Noise Robust Speech Recognition

Spectral Estimation for Noise Robust Speech Recognition

... SPECTRAL ESTIMATION FOR NOISE ROBUST SPEECH RECOGNITION SPECTRAL ESTIMATION FOR NOISE ROBUST SPEECH R E C O G N I T I O N Adoram Erell and Mitch Weintraub SRI International A B S T R A C T We present[.] ...

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HIERARCHICAL CLASSIFICATION TREE MODELING OF NONSTATIONARY NOISE FOR ROBUST SPEECH RECOGNITION

HIERARCHICAL CLASSIFICATION TREE MODELING OF NONSTATIONARY NOISE FOR ROBUST SPEECH RECOGNITION

... noise- robust speech ...noisy speech models ...clean speech and additive noise is trained in design ...actual recognition, both models are combined based on the current SNR ...

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Signal Processing for Robust Speech Recognition

Signal Processing for Robust Speech Recognition

... SIGNAL PROCESSING FOR ROBUST SPEECH RECOGNITION SIGNAL PROCESSING F O R ROBUST S P E E C H R E C O G N I T I O N Fu Hua Liu, Pedro J Moreno, Richard M Stem, Alejandro Acero Department of Electrical an[.] ...

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Multiple Approaches to Robust Speech Recognition

Multiple Approaches to Robust Speech Recognition

... MULTIPLE APPROACHES TO ROBUST SPEECH RECOGNITION MULTIPLE APPROACHES TO ROBUST SPEECH RECOGNITION Richard M Stern, Fu Hua Liu, Yoshiaki Ohshima, Thomas M Sullivan, Alejandro Acero* D e p a r t m e n t[.] ...

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Features and Model Adaptation Techniques for Robust Speech Recognition: A Review

Features and Model Adaptation Techniques for Robust Speech Recognition: A Review

... Another two model adaptation methods, the maximum likelihood linear regression (MLLR) [98] and maximum a posteriori (MAP) [77, 78], have been originally designed for adapting speaker independent acoustic models to a ...

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Multi-candidate missing data imputation for robust speech recognition

Multi-candidate missing data imputation for robust speech recognition

... We first formulated the missing data paradigm such that it can be applied to an acoustic model that requires no compromises on accuracy and uses standard feature representations, i.e., a formulation that covers cepstral ...

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A Comparative Study of Histogram Equalization (HEQ) for Robust Speech Recognition

A Comparative Study of Histogram Equalization (HEQ) for Robust Speech Recognition

... noisy) speech to that of the training (or reference) set, we found that some undesired sharp peaks or valleys of the feature vector component sequence caused by the non-stationary noises often occurring during the ...

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Robust Speech Recognition Using Factorial HMMs for Home Environments

Robust Speech Recognition Using Factorial HMMs for Home Environments

... In our proposed method, an HMM for each word in the dictionary and an HMM for sudden noise are created. Then, these models are combined to create an FHMM for each word. We propose an extension to employ dynamic features ...

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A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition

A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition

... the speech energy, the ITU rec- ommendation ...clean speech, namely white noise and coloured noise (obtained as lowpass filtered white ...ingful recognition accuracies: 5, 10, 15, 20, 25, and 30 ...

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Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition

... Current speech recognition technology offers the ideal complementary solution to more traditional visual and tactile man-machine ...art speech recognition systems perform well in the laboratory ...

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A Bayesian view on acoustic model based techniques for robust speech recognition

A Bayesian view on acoustic model based techniques for robust speech recognition

... In the past decades, numerous survey papers and books have been published summarizing the state-of-the-art in noise and reverberation-robust ASR [27, 31–36]. Recently, a comprehensive review of noise-robust ...

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A perceptual masking approach for noise robust speech recognition

A perceptual masking approach for noise robust speech recognition

... the speech signals ...to speech enhance- ment is considered in ...where speech frames are character- ized by high variance and noise frames by low variance, which are suppressed to improve the ASR ...

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Exploiting correlogram structure for robust speech recognition with multiple speech sources

Exploiting correlogram structure for robust speech recognition with multiple speech sources

... clean speech signal uttered by a female speaker, taken at time frames of 300 ms, 700 ms and 2100 ...voiced speech, then each frequency channel excited by that signal will have a high similarity to itself ...

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