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Enhancement of VSR using low dimension visual feature

机译:使用低维视觉功能增强VSR

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This paper presents a study about the low dimension visual (LDV) space features and investigates the improvement in audio visual automatic speech recognition using different set of visual features. The experiment is divided into three sub-sections; in first phase the recognition is performed on 12 static DCT features; in second phase the recognition is performed for combination of 6 static and 6 dynamic features and in third phase the recognition is performed on 12 low dimension DCT feature. For this research work Hindi AMUAV (Aligarh Muslim University Audio-Visual) database was developed in which audio sample at 44.1 kHz and video sample at 25 frames per second was opted. Hidden Markov Model (HMM) tool kit with left-right HMMs modeled was used for recognition and an overall improvement of 26.04%in word recognition is achieved with LDV space features.
机译:本文介绍了关于低维视觉(LDV)空间特征的研究,并使用不同一组视觉特征调查音频视觉自动语音识别的改进。实验分为三个子部分;在第一阶段,识别是对12个静态DCT特征进行的;在第二阶段中,对6个静态和6个动态特征的组合进行识别,并且在第三阶段中,在12个低维度DCT功能上执行识别。对于这项研究工作,开发了Hindi Amuav(Aligarh穆斯林大学视听)数据库,其中44.1 kHz的音频样本和每秒25帧的视频样本进行了选择。带有左右HMMS模型的隐马尔可夫模型(HMM)工具套件用于识别,通过LDV空间特征实现了26.04%的单词识别中的总体改进。

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