AVAS: Speech database for multimodal recognition applications
| Autor: | Mohamed F. Tolba, Saleh Aly, Alaa Sagheer, Samar Antar |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Audio mining
Voice activity detection Computer science business.industry Speech recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Acoustic model Audio-visual speech recognition Speech processing Facial recognition system Domain (software engineering) Computer vision Speech analytics Artificial intelligence business |
| Zdroj: | HIS |
| DOI: | 10.1109/his.2013.6920467 |
| Popis: | Audio-visual speech recognition (AVSR) systems represent an important branch in the human computer interaction (HCI) domain, since it is the simplest way to interact with computer. However, difficulties due to visual variations in video sequence can significantly degrade the recognition performance of AVSR systems. Although several corpuses have been created in this area, most of them are not include realistic visual variations in video sequence. This paper presents the first Audio-Visual Speech recognition corpus using Arabic language denoted as AVAS. All AVAS samples contain two of the most important visual variations; illumination variations and head pose variations, in the same video recording. Hence, AVAS is useful in the development of robust AVSR systems, automatic speech recognition “audio-only” systems, lip-reading “visual-only” systems and face recognition across pose and illumination variations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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