Name
Abstract | ||
|---|---|---|
This article introduces automatic speech recognition based on Electro-Magnetic Articulography (EMA). Movements of the tongue, lips, and jaw are tracked by an EMA device, which are used as features to create Hidden Markov Models (HMM) and recognize speech only from articulation, that is, without any audio information. Also, automatic phoneme recognition experiments are conducted to examine the contribution of the EMA parameters to robust speech recognition. Using feature fusion, multistream HMM fusion, and late fusion methods, noisy audio speech has been integrated with EMA speech and recognition experiments have been conducted. The achieved results show that the integration of the EMA parameters significantly increases an audio speech recognizer's accuracy, in noisy environments. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICME.2010.5583086 | 2010 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME 2010) |
Keywords | Field | DocType |
hidden markov models,electro magnetic,sensor fusion,hidden markov model,accuracy,speech recognition,data fusion,noise measurement,automatic speech recognition,speech | Feature fusion,Noise measurement,Pattern recognition,Computer science,Speech recognition,Sensor fusion,Artificial intelligence,Hidden Markov model,Phoneme recognition,Acoustic model | Conference |
ISSN | Citations | PageRank |
1945-7871 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Panikos Heracleous | 1 | 68 | 16.27 |
| pierre badin | 2 | 232 | 35.83 |
| Gérard Bailly | 3 | 609 | 99.37 |
| Norihiro Hagita | 4 | 2877 | 259.10 |