2017 - IEEE Fellow For contributions to speech recognition
His primary scientific interests are in Artificial intelligence, Speech recognition, Pattern recognition, Feature extraction and Speech processing. Bhiksha Raj has included themes like Machine learning and Non-negative matrix factorization in his Artificial intelligence study. He interconnects Speech enhancement, Vocabulary and Robustness in the investigation of issues within Speech recognition.
His work carried out in the field of Pattern recognition brings together such families of science as Feature, Spectrogram, Source separation, Latent variable model and Noise reduction. In his study, Voice search, Missing data, Beamforming and Array processing is strongly linked to Voice activity detection, which falls under the umbrella field of Feature extraction. His Speech processing research includes elements of Microphone array processing and Word error rate.
His primary areas of investigation include Speech recognition, Artificial intelligence, Pattern recognition, Machine learning and Speech processing. His research on Speech recognition frequently connects to adjacent areas such as Noise. His studies link Natural language processing with Artificial intelligence.
His Pattern recognition study incorporates themes from Feature, Signal, Feature and Non-negative matrix factorization. His research on Machine learning focuses in particular on Supervised learning. His research combines Speech enhancement and Speech processing.
Bhiksha Raj focuses on Artificial intelligence, Speech recognition, Artificial neural network, Machine learning and Pattern recognition. His studies in Artificial intelligence integrate themes in fields like Communication channel and Natural language processing. His specific area of interest is Speech recognition, where Bhiksha Raj studies Spectrogram.
His Artificial neural network research is multidisciplinary, incorporating elements of Algorithm, Metadata and Hidden Markov model. His study in the fields of Softmax function and Discriminative model under the domain of Pattern recognition overlaps with other disciplines such as Laplace operator. His Convolutional neural network study deals with Feature intersecting with Feature extraction.
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SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu;Yandong Wen;Zhiding Yu;Ming Li.
computer vision and pattern recognition (2017)
A vector Taylor series approach for environment-independent speech recognition
P.J. Moreno;B. Raj;R.M. Stern.
international conference on acoustics speech and signal processing (1996)
Sphinx-4: a flexible open source framework for speech recognition
Willie Walker;Paul Lamere;Philip Kwok;Bhiksha Raj.
DCASE 2017 challenge setup: tasks, datasets and baseline system
Annamaria Mesaros;Toni Heittola;Aleksandr Diment;Benjamin Martinez Elizalde.
DCASE 2017 - Workshop on Detection and Classification of Acoustic Scenes and Events (2017)
Speech denoising using nonnegative matrix factorization with priors
K.W. Wilson;B. Raj;P. Smaragdis;A. Divakaran.
international conference on acoustics, speech, and signal processing (2008)
A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research
Keisuke Kinoshita;Marc Delcroix;Sharon Gannot;Emanuël A. P. Habets.
EURASIP Journal on Advances in Signal Processing (2016)
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition
Zhenzhong Lan;Ming Lin;Xuanchong Li;Alexander G. Hauptmann.
computer vision and pattern recognition (2015)
Supervised and semi-supervised separation of sounds from single-channel mixtures
Paris Smaragdis;Bhiksha Raj;Madhusudana Shashanka.
international conference on independent component analysis and signal separation (2007)
Missing-feature approaches in speech recognition
B. Raj;R.M. Stern.
IEEE Signal Processing Magazine (2005)
Reconstruction of missing features for robust speech recognition
Bhiksha Raj;Michael L. Seltzer;Richard M. Stern.
Speech Communication (2004)
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