What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- 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 most cited work include:
- SphereFace: Deep Hypersphere Embedding for Face Recognition (1077 citations)
- A vector Taylor series approach for environment-independent speech recognition (418 citations)
- Sphinx-4: a flexible open source framework for speech recognition (366 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Speech recognition (48.03%)
- Artificial intelligence (46.35%)
- Pattern recognition (26.40%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (46.35%)
- Speech recognition (48.03%)
- Artificial neural network (6.74%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- SphereFace: Deep Hypersphere Embedding for Face Recognition (1077 citations)
- SphereFace: Deep Hypersphere Embedding for Face Recognition (228 citations)
- DCASE 2017 Challenge setup: Tasks, datasets and baseline system (168 citations)
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