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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Deshmukh, N. Ngan, J. Hamaker, J. Picone, J. |
| Copyright Year | 1997 |
| Description | Author affiliation: Inst. for Signal & Inf. Process., Mississippi State Univ., MS, USA (Deshmukh, N.) |
| Abstract | Accurate recognition of proper nouns is a critical component of automatic speech recognition (ASR). Since there are no obvious letter-to-sound conversion rules that govern the pronunciation of any large set of proper nouns, this is an open-ended problem that evolves constantly under various sociolinguistic influences. A Boltzmann machine neural network is well-suited for the task of generating the most likely pronunciations of a proper noun. This pronunciation output can be used to build better acoustic models for the noun that result in improved recognition performance. We present an advanced version of this N-best pronunciations system; and a multiple pronunciations dictionary of 18000 surnames and 25000 pronunciations used as a training database. The database and software are available in the public domain. |
| Starting Page | 1467 |
| Ending Page | 1470 |
| File Size | 401062 |
| Page Count | 4 |
| File Format | |
| ISBN | 0818679190 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.1997.596226 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1997-04-21 |
| Publisher Place | Germany |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Automatic speech recognition Neurons Databases Simulated annealing Neural networks Backpropagation Computational modeling Signal processing Information processing Dictionaries |
| Content Type | Text |
| Resource Type | Article |
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