Abstract-In this paper, we present an unsupervised learning framework to address the problem of detecting spoken keywords. Without any transcription information, a Gaussian Mixture Model is trained to label speech frames with a Gaussian posteriorgram. Given one or more spoken examples of a keyword, we use segmental dynamic time warping to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all the test utterances. We examine the TIMIT corpus as a development set to tune the parameters in our system, and the MIT Lecture corpus for more substantial evaluation. The results demonstrate the viability and effectiveness of our unsupervised learning framework on the keyword spotting task.

The prevalence of MCI affected by sex, age,education, which was lower in eastern than that in western China.

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Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then it is unrolled to autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.Index Terms-robust speech recognition, feature denoising, denoising autoencoder, deep neural network

The prevalence of depressive symptoms among the older in China was a little high, and it was varied by gender, age, area, and education level.

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