论文标题
使用相关加权表示的强大的原始波形语音识别
Robust Raw Waveform Speech Recognition Using Relevance Weighted Representations
论文作者
论文摘要
嘈杂和通道扭曲的情景中的语音识别通常是具有挑战性的，因为当前的声学建模方案并不适应噪声存在下信号分布的变化。在这项工作中，我们开发了一个新型的声学建模框架，以基于相关性加权机制来稳健的语音识别。相关加权是使用执行特征选择的子网络方法实现的。相关性子网络应用于在原始语音信号上运行的卷积网络模型的第一层的输出，而第二个相关性子网络则应用于第二卷积层输出。第一层的相关权重对应于声学过滤库的选择，而第二层的相关权重执行调制过滤器选择。该模型接受了有关嘈杂和混响的语音的语音识别任务的培训。在多个数据集（Aurora-4，Chime-3，声音）上进行的语音识别实验表明，在神经网络体系结构中相关性加权的结合显着提高了语音识别单词错误率（平均相对相对提高了10％的估算基线系统）
Speech recognition in noisy and channel distorted scenarios is often challenging as the current acoustic modeling schemes are not adaptive to the changes in the signal distribution in the presence of noise. In this work, we develop a novel acoustic modeling framework for noise robust speech recognition based on relevance weighting mechanism. The relevance weighting is achieved using a sub-network approach that performs feature selection. A relevance sub-network is applied on the output of first layer of a convolutional network model operating on raw speech signals while a second relevance sub-network is applied on the second convolutional layer output. The relevance weights for the first layer correspond to an acoustic filterbank selection while the relevance weights in the second layer perform modulation filter selection. The model is trained for a speech recognition task on noisy and reverberant speech. The speech recognition experiments on multiple datasets (Aurora-4, CHiME-3, VOiCES) reveal that the incorporation of relevance weighting in the neural network architecture improves the speech recognition word error rates significantly (average relative improvements of 10% over the baseline systems)
