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Alzheimer's disease is a common cognitive disorder in the elderly. Early and accurate diagnosis of Alzheimer's disease (AD) has a major impact on the progress of research on dementia. At present, researchers have used machine learning methods to detect Alzheimer's disease from the speech of participants. However, the recognition accuracy of current methods is unsatisfactory, and most of them focus on using low-dimensional handcrafted features to extract relevant information from audios. This paper proposes an Alzheimer's disease detection system based on the pre-trained framework Wav2vec 2.0 (Wav2vec2). In addition, by replacing the loss function with the Soft-Weighted CrossEntropy loss function, we achieved 85.45\% recognition accuracy on the same test dataset.

Speech Emotion Recognition (SER) is still a complex task for computers with average recall rates usually about 70% on the most realistic datasets. Most SER systems use hand-crafted features extracted from audio signal such as energy, zero crossing rate, spectral information, prosodic, mel frequency cepstral coefficient (MFCC), and so on. More recently, using raw waveform for training neural network is becoming an emerging trend. This approach is advantageous as it eliminates the feature extraction pipeline. Learning from time-domain signal has shown good results for tasks such as speech recognition, speaker verification etc. In this paper, we utilize Sinc-convolution layer, which is an efficient architecture for preprocessing raw speech waveform for emotion recognition, to extract acoustic features from raw audio signals followed by a long short-term memory (LSTM). We also incorporate linguistic features and append a dialogical emotion decoding (DED) strategy. Our approach achieves a weighted accuracy of 85.1\% in four class emotion on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset.