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Performance Analysis of Lip Synchronization Using LPC, MFCC and PLP Speech Parameters

机译:LPC,MFCC和PLP语音参数唇部同步性能分析

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Many multimedia applications and entertainment industry products like games, cartoons and film dubbing require speech driven face animation and audio-video synchronization. Only Automatic Speech Recognition system (ASR) does not give good results in noisy environment. Audio Visual Speech Recognition system plays vital role in such harsh environment as it uses both - audio and visual - information. In this paper, we have proposed a novel approach with enhanced performance over traditional methods that have been reported so far. Our algorithm works on the bases of acoustic and visual parameters to achieve better results. We have tested our system for English language using LPC, MFCC and PLP parameters of the speech. Lip parameters like lip width, lip height etc are extracted from the video and these both acoustic and visual parameters are used to train systems like Artificial Neural Network (ANN), Vector Quantization (VQ), Dynamic Time Warping (DTW), Support Vector Machine (SVM). We have employed neural network in our research work with LPC, MFCC and PLP parameters. Results show that our system is giving very good response against tested vowels.
机译:许多多媒体应用和娱乐行业产品如游戏,漫画和电影配音需要语音驱动的面部动画和音频视频同步。只有自动语音识别系统(ASR)在嘈杂的环境中没有给出良好的结果。视听语音识别系统在这种恶劣环境中起着重要作用,因为它使用 - 音频和视觉信息。在本文中,我们提出了一种提高迄今为止报告的传统方法表现的新方法。我们的算法适用于声学和视觉参数的基础,以实现更好的结果。我们使用LPC,MFCC和PLP参数来测试我们的英语系统。像唇宽,唇高等这样的唇部参数从视频中提取,这些声学和视觉参数都用于训练人工神经网络(ANN),矢量量化(VQ),动态时间翘曲(DTW),支持向量机等系统(SVM)。我们在我们的研究工作中使用了神经网络,使用LPC,MFCC和PLP参数。结果表明,我们的系统对测试元音的反应非常好。

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