computer aided instruction; hidden Markov models; natural language processing; neural nets; speech recognition; CSLU; Filipino speakers; HMM/ANN keyword spotting framework; MFCC; MLP; Mel frequency cepstral coefficients; Oregon Graduate Institute; center for spoken language and learning; constant frequency tone; feature extraction method; hybrid Hidden Markov Model/Artificial Neural Network; multilayer perceptron; profane Filipino words; small vocabulary automatic Filipino speech profanity suppression system; speech recognition; speech recognition architecture; viterbi beam search algorithm; vulgar Filipino words; Accuracy; Artificial neural networks; Databases; Hidden Markov models; Speech; Speech recognition; Testing; Artificial Neural Network (ANN); Hidden Markov Model (HMM); Mel Frequency Cepstral Coefficients (MFCC); Multi-layer Perceptron (MLP);
机译:使用隐马尔可夫模型(HMM)开发用于聋人或哑人菲律宾人的菲律宾手语的语言
机译:一种新的混合深层学习模型,基于人工神经网络和隐马尔可夫模型的推荐系统
机译:具有隐马尔可夫模型和径向基函数神经网络的混合语音识别系统。
机译:基于混合隐马尔可夫模型/人工神经网络(HMM / ANN)关键字发现框架的小词汇自动菲律宾语音亵渎抑制系统
机译:马尔可夫链蒙特卡洛贝叶斯预测框架用于人工神经网络委员会建模和仿真。
机译:大型词汇自动语音识别深尖峰神经网络
机译:说话人识别系统中连续隐马尔可夫模型(CHMM)与人工神经网络(ANN)的比较研究