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Robust Features in Deep-Learning-Based Speech Recognition.

, , , , , , , , , and . New Era for Robust Speech Recognition, Exploiting Deep Learning, Springer, (2017)

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Articulatory Information for Noise Robust Speech Recognition., , , , and . IEEE Trans. Speech Audio Process., 19 (7): 1913-1924 (2011)Medium-duration modulation cepstral feature for robust speech recognition., , , and . ICASSP, page 1749-1753. IEEE, (2014)Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice., , , , , , , , and . INTERSPEECH, page 1651-1655. ISCA, (2019)Multi-Modal Learning for Speech Emotion Recognition: An Analysis and Comparison of ASR Outputs with Ground Truth Transcription., , , and . INTERSPEECH, page 3302-3306. ISCA, (2019)Noise Robust Acoustic to Articulatory Speech Inversion., , , and . INTERSPEECH, page 3137-3141. ISCA, (2018)A procedure for estimating gestural scores from natural speech., , , , , , and . INTERSPEECH, page 30-33. ISCA, (2010)The SRI System for the NIST OpenSAD 2015 Speech Activity Detection Evaluation., , and . INTERSPEECH, page 3673-3677. ISCA, (2016)Coping with Unseen Data Conditions: Investigating Neural Net Architectures, Robust Features, and Information Fusion for Robust Speech Recognition., and . INTERSPEECH, page 3783-3787. ISCA, (2016)Strategies for high accuracy keyword detection in noisy channels., , , , , , , , , and 1 other author(s). INTERSPEECH, page 15-19. ISCA, (2013)Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition., and . CoRR, (2017)