What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Speech recognition
His primary areas of investigation include Speech recognition, Artificial intelligence, Natural language processing, Vocabulary and Artificial neural network.
His Speech recognition research includes themes of Word and Phone.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition.
His Natural language processing research incorporates themes from Context, Keyword search, Search engine indexing and Speaker adaptation.
Within one scientific family, he focuses on topics pertaining to Task under Vocabulary, and may sometimes address concerns connected to Perceptron, Variety and Speaker recognition.
His studies deal with areas such as Pooling and Convolutional neural network as well as Artificial neural network.
His most cited work include:
- Deep convolutional neural networks for LVCSR (728 citations)
- Low-rank matrix factorization for Deep Neural Network training with high-dimensional output targets (414 citations)
- Boosted MMI for model and feature-space discriminative training (341 citations)
What are the main themes of his work throughout his whole career to date?
Bhuvana Ramabhadran focuses on Artificial intelligence, Speech recognition, Natural language processing, Language model and Word error rate.
His Artificial intelligence research integrates issues from Machine learning, Vocabulary and Pattern recognition.
His Speech recognition study frequently links to adjacent areas such as Feature.
His work is dedicated to discovering how Natural language processing, Speech corpus are connected with Speech technology and other disciplines.
His n-gram study in the realm of Language model interacts with subjects such as Cache language model.
His Word error rate research incorporates elements of Natural language and Phone.
He most often published in these fields:
- Artificial intelligence (62.63%)
- Speech recognition (58.59%)
- Natural language processing (31.99%)
What were the highlights of his more recent work (between 2017-2021)?
- Speech recognition (58.59%)
- Artificial intelligence (62.63%)
- Speech synthesis (8.42%)
In recent papers he was focusing on the following fields of study:
Bhuvana Ramabhadran mostly deals with Speech recognition, Artificial intelligence, Speech synthesis, Language model and Word error rate.
His specific area of interest is Speech recognition, where Bhuvana Ramabhadran studies Utterance.
Specifically, his work in Artificial intelligence is concerned with the study of Artificial neural network.
His Speech synthesis study combines topics from a wide range of disciplines, such as Embedding, Prosody, Selection and Cloning.
His Language model research also works with subjects such as
- Recurrent neural network and Sentence most often made with reference to Word,
- Principle of maximum entropy which intersects with area such as Vocabulary and Context.
He has researched Word error rate in several fields, including Pronunciation, Transliteration, Acoustic model and Hidden Markov model, Pattern recognition.
Between 2017 and 2021, his most popular works were:
- Building Competitive Direct Acoustics-to-Word Models for English Conversational Speech Recognition (86 citations)
- Speech Recognition with Augmented Synthesized Speech (28 citations)
- Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Speech recognition
His primary scientific interests are in Speech recognition, Speech synthesis, Word error rate, Language model and Natural language processing.
Bhuvana Ramabhadran does research in Speech recognition, focusing on Utterance specifically.
His study in Word error rate is interdisciplinary in nature, drawing from both Acoustic model and Hidden Markov model.
In his study, Initialization, Context, Recurrent neural network, Principle of maximum entropy and Sentence is strongly linked to Word, which falls under the umbrella field of Language model.
His Natural language processing research includes elements of Mandarin Chinese, Foreign language, Artificial intelligence and Representation.
The study incorporates disciplines such as Identity and Cloning in addition to Artificial intelligence.
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