Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
- Authors
- C.J. LeggetterPhilip C. Woodland
- Journal
- Computer Speech & Language
In The Last Decade
doi.org/10.1006/csla.1995.0010 →Countries where authors are citing Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
This map shows the geographic impact of Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models more than expected).
Fields of papers citing Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
This network shows the impact of Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models.
About Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
This paper, published in 1995, received 1.6k indexed citations . Written by C.J. Leggetter and Philip C. Woodland covering the research area of Artificial Intelligence and Signal Processing. It is primarily cited by scholars working on Artificial Intelligence (1.5k citations), Signal Processing (1.3k citations) and Computer Vision and Pattern Recognition (186 citations). Published in Computer Speech & Language.
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This paper is also available at doi.org/10.1006/csla.1995.0010.