robust speech recognition features
Enhancing the magnitude spectrum of speech features for robust speech recognition
... resulting speech features are more ...each speech frame is amplified by multiplying by a weighting factor that is related to the signal-to-noise ratio ...the speech and non-speech ...
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Robust Features for Automatic Text-Independent Emotion Recognition from Speech
... verbalization features like energy profile, intonation pattern, duration variations and formant tracks, to perceive and process the emotional content from the verbalization utterances (Benesty et ...prosodic ...
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A novel noise robust speaker identification system
... To reduce the mismatch between training and test conditions [1] speakers can be modeled in multiple noisy environments. CurrentlySpeech enhancement methods are spectral subtraction [2], noise-robust speaker ...
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Noise Cancellation Method for Robust Speech Recognition
... from speech signals in wavelet transform domain ...two features, the slow time-varying nature and the Lipschitz regularity of the speech ...with speech signals and results show the method is ...
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IMAGE DUPLICATION AND ROTATION DETECTION METHODS FOR STORAGE UTILIZATION
... of speech recognition is to compress a speech signal into streams of acoustic feature vectors, referred to as speech feature ...the speech signal into feature vectors; secondly is to ...
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VDCNN based Noise Robust Speech Recognition with Combination of GMM and MFCC Features
... the speech recognition systems require priori knowledge of twists and conditions in which it needs to ...automatic speech recognition in real time ...bank features (GFBs) and normalized ...
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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 ...
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Automatic Speaker Recognition System in Adverse Conditions — Implication of Noise and Reverberation on System Performance
... Abstract—Speaker recognition has been developed and evolved over the past few decades into a supposedly mature ...utilize robust features extracted from clean ...of recognition becomes ...
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Speech/Non-Speech Segmentation Based on Phoneme Recognition Features
... for speech and non-speech segmentation of audio data and proposes a new, high-level representation of audio signals based on phoneme recognition features suitable for ...
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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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Stereo-based histogram equalization for robust speech recognition
... environments. Speech enhancement methods can be classified into two main categories ...resistant features, feature normalization, and feature ...approach, robust signal processing is employed to ...
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Design and Development of Silent Speech Recognition System for Monitoring of Devices
... There are endless possibilities for improvements but some of which that have come necessary to us during the development phase are as follows. In the future we would be able to add more electrodes. Addition of electrodes ...
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Comparative Study of Diverse Face Recognition Approaches along with Intrinsic Worth and Recognition Rate
... The recognition rate of PCA-based face recognition outperforms when the number of test images increases, but the rate of recognition decreases of the certain ...the recognition data ...
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Speech Emotion Recognition Systems: Review
... emotion recognition from speech by incorporating rhythm and temporal ...Emotion Recognition researches are mainly based on applying features like MFCC’s, pitch and ...rhythm features ...
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Exploiting correlogram structure for robust speech recognition with multiple speech sources
... the speech fragment decoding system (after Barker et ...to speech models is then used to search for the most likely combination of fragment labelling and speech model ...ceiving speech signals ...
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Speech Endpoint Detection Based on High Order Statistics
... of speech utterance and exclusion of the non-speech segments by digital processing ...in speech coding and speech ...of speech based applications. The recognition performance has ...
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An Analysis of Visual Speech Features for Recognition of Non articulatory Sounds using Machine Learning
... of speech disorders requires speech therapy and substantial ...of speech. Speech training starts with facial motor praxia activities and oral myofunctional exercises that involve production of ...
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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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A Unified Approach in Speech to Speech Translation: Integrating Features of Speech recognition and Machine Translation
... the speech recognizer gen- erated N -best (N =100) recognition hypothe- ses for each test speech ...shows speech recognition results of the test data set in single-best and N -best ...
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Recent Progress in Robust Vocabulary Independent Speech Recognition
... Recent Progress in Robust Vocabulary Independent Speech Recognition Recent Progress in Robust Vocabulary Independent Speech Recognition Hsiao Wuen Hon and Kai Fu Lee School o f Computer Science Carneg[.] ...
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