An Attention Based Deep Neural Network for Automatic Lexical Stress Detection Lexical stress detection is one of important tasks in selfdirected language learning application. We address this task by leveraging two successful attention techniques in natural language processing, inner attention and selfattention. First, combined with LSTM to model timeseries features, inner attention could extract most important information and then convert lengthvarying input into a fixedlength feature vector; Second, selfattention intrinsically supports words with different number of syllables as input to model contexture information. Besides, our model is straightforward to expand to include handcrafted features to further improve performance, and also can be applied to similar tasks, such as pitch accent detector. Experiments on LibriSpeech, TedLium and a third selfrecored datasets show the high performance of our proposed attention based neural network.

Emotion Recognition from Children Speech Signals Using Attention Based Time Series Deep Learning Childrenu0027s emotions expression concentrates in the acoustic aspects such as the tones and timbres of the voice instead of the semantics, and there are a lot of lengthy fragments in their speech. This paper proposes an emotion recognition model using the time series deep learning technology, named attention based Bidirectional Long ShortTerm Memory (CNNBiLSTM) to extract the emotional features. After preprocessing the speech signal, the fortydimensional Mel Frequency Cepstral Coefficients (MFCC) related parameters are extracted, including the dynamic and static features. And these frequency domain features are enhanced by convolutional neural networks (CNNs) as the emotional features of childrenu0027s speech recognition. BiLSTM is used to solve the problem of poor performance of longterm dependent learning features, and attention mechanism is used for only a few frames contain emotional features in the children speech signal. Compared with the related speech emotion recognition models such as LSTMCNN and 2DCNNLSTM, our proposed speech emotion recognition model improves the accuracy up to 71.6% on the FAUAIBO childrenu0027s speech emotion database.