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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Reddy, B.V.S. Rao, K.V. Prasanna, S.R.M. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electron. & Commun. Eng., Indian Inst. of Technol., Guwahati (Reddy, B.V.S.; Rao, K.V.; Prasanna, S.R.M.) |
| Abstract | This work demonstrates the development of Keyword Spotting (KWS) system using Vowel Onset Point (VOP), Vector Quantization (VQ) and Hidden Markov Model(HMM) based techniques. The goal of KWS system is to spot the keywords present in the test speech signal, while neglecting rest of the words. In this work, first independent KWS systems will be developed using VOP, VQ and HMM techniques. Each of these methods involve different techniques and hence it may be possible to combine them for achieving higher performance. In the next step, KWS system is also developed by combining HMM and VQ (HMM-VQ) and also HMM and VOP (HMM-VOP) based KWS systems. The performance measured in terms of Figure Of Merit (FOM) for HMM, VQ and VOP are 53.32, 22.41 and 26.95, respectively. The FOM of combinations HMM-VQ and HMM-VOP are 57.18 and 60.62, respectively. The significantly improved performance in the combined systems demonstrate the complementary nature of keyword information exploited by each of the independent systems. |
| Sponsorship | IEEE Region 10 |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 68332 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424424085 |
| DOI | 10.1109/TENCON.2008.4766793 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-19 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Vector quantization Speech recognition Humans Automatic speech recognition Natural languages System testing Training data Buildings Signal processing |
| Content Type | Text |
| Resource Type | Article |
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