2013 - IEEE Fellow For contributions to large vocabulary speech recognition
His primary scientific interests are in Speech recognition, Artificial intelligence, Word error rate, Hidden Markov model and Natural language processing. His Speech recognition study incorporates themes from Mutual information, Vocabulary and Cluster analysis. His Artificial intelligence research is multidisciplinary, incorporating elements of Decoding methods and Pattern recognition.
His Word error rate research focuses on subjects like Phone, which are linked to Reduction and Smoothing. The concepts of his Hidden Markov model study are interwoven with issues in Decision tree, Context and Markov model. His Natural language processing study integrates concerns from other disciplines, such as Acoustic model and Speech synthesis.
Philip C. Woodland mainly investigates Speech recognition, Artificial intelligence, Natural language processing, Word error rate and Hidden Markov model. His Speech recognition research integrates issues from Word, Artificial neural network, Vocabulary and Discriminative model. The study incorporates disciplines such as Context, Machine learning and Pattern recognition in addition to Artificial intelligence.
His Natural language processing research includes elements of Transcription, Speech corpus and Mandarin Chinese. His Word error rate research is multidisciplinary, incorporating perspectives in Speaker diarisation, Reduction, Transcription, Acoustic model and Robustness. Philip C. Woodland combines subjects such as Decision tree, Estimation theory, Markov model and Speech synthesis with his study of Hidden Markov model.
Philip C. Woodland mainly focuses on Speech recognition, Artificial intelligence, Word error rate, Artificial neural network and Language model. His Speech recognition study deals with Word intersecting with Sigmoid function. He has included themes like Natural language processing, Machine learning and Pattern recognition in his Artificial intelligence study.
His Word error rate research is multidisciplinary, relying on both Transcription, Acoustic model, Transcription and Embedding. His research in Artificial neural network intersects with topics in Algorithm, Discriminative model and Hidden Markov model. His study looks at the intersection of Hidden Markov model and topics like Lexicon with Connectionism.
His scientific interests lie mostly in Speech recognition, Artificial intelligence, Artificial neural network, Word error rate and Language model. His Speech recognition study combines topics from a wide range of disciplines, such as Decoding methods, Activation function and Hybrid system. Philip C. Woodland interconnects Machine learning, Vocabulary and Pattern recognition in the investigation of issues within Artificial intelligence.
His studies in Artificial neural network integrate themes in fields like Feature extraction and Cluster analysis. His Word error rate study integrates concerns from other disciplines, such as Acoustic model and Reduction. His research investigates the connection between Language model and topics such as Recurrent neural network that intersect with issues in Natural language processing, Class and Source code.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The HTK book
SJ Young;J Jansen;JJ Odell;DG Ollason.
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
C. J. Leggetter;Philip C. Woodland.
Computer Speech & Language (1995)
Tree-Based State Tying for High Accuracy Modelling.
Steve J. Young;J. J. Odell;Philip C. Woodland.
Tree-based state tying for high accuracy acoustic modelling
S. J. Young;J. J. Odell;P. C. Woodland.
human language technology (1994)
The HTK book version 3.4
SJ Young;G Evermann;Mjf Gales;D Kershaw.
Minimum Phone Error and I-smoothing for improved discriminative training
D. Povey;P.C. Woodland.
international conference on acoustics, speech, and signal processing (2002)
Mean and variance adaptation within the MLLR framework
Mark J. F. Gales;Philip C. Woodland.
Computer Speech & Language (1996)
Large scale discriminative training of hidden Markov models for speech recognition
P.C. Woodland;D. Povey.
Computer Speech & Language (2002)
Large vocabulary continuous speech recognition using HTK
P.C. Woodland;J.J. Odell;V. Valtchev;S.J. Young.
international conference on acoustics, speech, and signal processing (1994)
MMIE training of large vocabulary recognition systems
V. Valtchev;J. J. Odell;P. C. Woodland;S. J. Young.
Speech Communication (1997)
Profile was last updated on December 6th, 2021.
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