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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rose, R. Momayyez, P. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, Que., Canada (Rose, R.; Momayyez, P.) |
| Abstract | The main goal of this paper is to investigate the feasibility of exploiting the invariance properties associated with articulatory based acoustic features to reduce ambiguity in ASR search. A multivalued phonological feature set defined by King and Taylor is used along with a time delay neural network implementation of phonological feature detectors to produce eight independent phonological feature streams (S. King and P. Taylor, 2000). Hidden Markov models (HMMs) defined over these phonological feature streams are combined with HMMs defined over spectral energy based mel frequency cepstrum coefficient (MFCC) acoustic features through a lattice re-scoring procedure. It is shown that significant improvements in phone recognition accuracy are obtained for this combined system relative to phone accuracy obtained for MFCC based HMMs alone. A study is also performed to analyze the effects of uncertainty in phonological feature detection. |
| File Size | 132781 |
| File Format | |
| ISBN | 1424407273 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2007.366915 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-15 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Automatic speech recognition Hidden Markov models Computer vision Acoustic signal detection Mel frequency cepstral coefficient Delay effects Neural networks Detectors Cepstrum Lattices Phonological Features Speech Recognition Acoustic Modeling |
| Content Type | Text |
| Resource Type | Article |
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