Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.
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Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.
Imprint | Springer-Verlag New York |
Country of origin | United States |
Series | Lecture Notes in Electrical Engineering, 42 |
Release date | November 2010 |
Availability | Expected to ship within 10 - 15 working days |
First published | 2009 |
Authors | Sakriani Sakti, Konstantin Markov, Satoshi Nakamura, Wolfgang Minker |
Dimensions | 235 x 155 x 11mm (L x W x T) |
Format | Paperback |
Pages | 196 |
Edition | Softcover reprint of hardcover 1st ed. 2009 |
ISBN-13 | 978-1-4419-4676-8 |
Barcode | 9781441946768 |
Categories | |
LSN | 1-4419-4676-4 |