What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Speech recognition, Artificial intelligence, Artificial neural network, Natural language processing and Machine learning.
He has included themes like Time delay neural network and Robustness in his Speech recognition study.
He has included themes like Vocabulary, Task and Pattern recognition in his Artificial intelligence study.
His Artificial neural network study combines topics in areas such as Speaker verification and Lattice, Topology.
The Machine learning study combines topics in areas such as Work, Probability distribution and Training set.
His Perplexity study in the realm of Language model interacts with subjects such as Scalability.
His most cited work include:
- Recurrent neural network based language model (3552 citations)
- Librispeech: An ASR corpus based on public domain audio books (1658 citations)
- Extensions of recurrent neural network language model (1183 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Speech recognition, Natural language processing, Language model and Machine translation.
The Artificial intelligence study which covers Pattern recognition that intersects with Set.
His Speech recognition study incorporates themes from Artificial neural network, Vocabulary and Word.
His Natural language processing research incorporates elements of Pronunciation, Speech corpus and Mandarin Chinese.
His work on Perplexity as part of general Language model research is frequently linked to Cache language model, thereby connecting diverse disciplines of science.
His Machine translation research is multidisciplinary, relying on both Translation, Parsing and Rule-based machine translation.
He most often published in these fields:
- Artificial intelligence (53.54%)
- Speech recognition (51.18%)
- Natural language processing (31.65%)
What were the highlights of his more recent work (between 2018-2021)?
- Speech recognition (51.18%)
- Artificial intelligence (53.54%)
- Speaker diarisation (7.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Speech recognition, Artificial intelligence, Speaker diarisation, Language model and Artificial neural network.
His study in Speaker recognition and Word error rate is done as part of Speech recognition.
His Artificial intelligence study integrates concerns from other disciplines, such as Natural language processing, Set and Pattern recognition.
Sanjeev Khudanpur combines subjects such as Cluster analysis, Conversational speech and Voice activity detection with his study of Speaker diarisation.
A large part of his Language model studies is devoted to Perplexity.
His Artificial neural network study combines topics from a wide range of disciplines, such as Transcription, Baseline and Utterance.
Between 2018 and 2021, his most popular works were:
- Speaker Recognition for Multi-speaker Conversations Using X-vectors (94 citations)
- CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings (49 citations)
- State-of-the-Art Speaker Recognition for Telephone and Video Speech: The JHU-MIT Submission for NIST SRE18. (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Speech recognition, Speaker diarisation, Speaker recognition, Artificial neural network and Language model.
His research integrates issues of Time delay neural network and Open source in his study of Speech recognition.
His Artificial neural network research includes elements of Baseline and Hidden Markov model.
His studies deal with areas such as Text corpus, Empirical research, Transformer and Machine translation as well as Language model.
Machine translation is a subfield of Artificial intelligence that Sanjeev Khudanpur explores.
His biological study spans a wide range of topics, including Embedding and Measure.
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