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Robust Speech Recognition Using Fusion Techniques and Adaptive Filtering
| Content Provider | Semantic Scholar |
|---|---|
| Author | Al-Haddad, S. A. R. Samad, Salina Abdul Hussain, Aini Ishak, Khairul Anuar Noor, Ali O. Abid |
| Copyright Year | 2008 |
| Abstract | The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). Speech signals are often corrupted with background noise and the changes in signal characteristics could be fast. These issues are especially important for robust speech recognition. Robustness is a key issue in speech recognition. The algorithm is tested on speech samples that are a part of a Malay corpus. It is shown that the fusion technique can be used to fuse the pattern recognition outputs of DTW and HMM. Furthermore refinement normalization was introduced by using weight mean vector to obtain better performance. Accuracy of 94% on pattern recognition was obtainable using fusion HMM and DTW compared to 80.5% using DTW and 90.7% using HMM separately. The accuracy of the proposed algorithm is increased further to 98% by utilization the RLS adaptive noise cancellation. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://msol.people.uic.edu/ECE516/papers/Robust%20Speech%20Recognition.pdf |
| Alternate Webpage(s) | https://thescipub.com/pdf/10.3844/ajassp.2009.290.295 |
| Alternate Webpage(s) | http://www.scipub.org/fulltext/ajas/ajas62290-295.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Adaptive filter Algorithm Body of uterus Dynamic time warping Hidden Markov model Least-Squares Analysis Markov chain Part-of-speech tagging Pattern recognition Recursion Recursive least squares filter Refinement (computing) Restless Legs Syndrome Speech recognition |
| Content Type | Text |
| Resource Type | Article |
