Differentiable Pooling for Unsupervised Acoustic Model Adaptation

We present a deep neural network (DNN) acoustic model that includes parametrised and differentiable pooling operators. Unsupervised acoustic model adaptation is cast as the problem of updating the decision boundaries implemented by each pooling operator. In particular, we experiment with two types of pooling parametrisations: learned $L_p$-norm pooling and weighted Gaussian pooling, in which the weights of both operators are treated as speaker-dependent. We perform investigations using three different large vocabulary speech recognition corpora: AMI meetings, TED talks and Switchboard conversational telephone speech. We demonstrate that differentiable pooling operators provide a robust and relatively low-dimensional way to adapt acoustic models, with relative word error rates reductions ranging from 5--20% with respect to unadapted systems, which themselves are better than the baseline fully-connected DNN-based acoustic models. We also investigate how the proposed techniques work under various adaptation conditions including the quality of adaptation data and complementarity to other feature- and model-space adaptation methods, as well as providing an analysis of the characteristics of each of the proposed approaches.Comment: 11 pages, 7 Tables, 7 Figures in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, num. 11, 201

Half Gaussian-based wavelet transform for pooling layer for convolution neural network

Pooling methods are used to select most significant features to be aggregated to small region. In this paper, anew pooling method is proposed based on probability function. Depending on the fact that, most information is concentrated from mean of the signal to its maximum values, upper half of Gaussian function is used to determine weights of the basic signal statistics, which is used to determine the transform of the original signal into more concise formula, which can represent signal features, this method named half gaussian transform (HGT). Based on strategy of transform computation, Three methods are proposed, the first method (HGT1) is used basic statistics after normalized it as weights to be multiplied by original signal, second method (HGT2) is used determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian, while the third method (HGT3) is worked in similar to (HGT1) except, it depend on entire signal. The proposed methods are applied on three databases, which are (MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results show that, our methods are achieved good improvement, which is outperformed standard pooling methods such as max pooling and average pooling