Abstract: A speech synthesis method includes: obtaining an acoustic feature sequence of a text to be processed; processing the acoustic feature sequence by using a non-autoregressive computing model in parallel to obtain first audio information of the text to be processed, wherein the first audio information comprises audio corresponding to each segment; processing the acoustic feature sequence and the first audio information by using an autoregressive computing model to obtain a residual value corresponding to each segment; and obtaining second audio information corresponding to an i-th segment based on the first audio information corresponding to the i-th segment and the residual values corresponding to a first to an (i?1)-th segment, wherein a synthesized audio of the text to be processed comprises each of the second audio information, i=1, 2 . . . n, n is a total number of the segments.

Abstract: Techniques for adjusting outlier datasets for training chatbot systems in natural language processing are disclosed. In one particular aspect, a method is provided that includes receiving a dataset that includes training or inference data. An initial set of outlier data points can be identified within the dataset based on a score of the outlier data points being above or below a threshold. The initial set can be adjusted by identifying one or more nearest neighbors, which can be included in the dataset. Outlier data points that include a label that matches a number of labels of the nearest neighbors that exceeds a predetermined threshold can be removed from the initial set of outlier data points to generate a final set. Outlier data points of the final set can be adjusted with respect to the dataset to generate a set of training data that is used to train a machine-learning model.

Abstract: A concealed text feature corresponding to a text data block of a plurality of text data blocks included in the text data and at least one concealed text feature corresponding to at least one text data block subsequent to the text data block are generated. A coarse fusion is performed on (i) the concealed text feature corresponding to the text data block and (ii) the at least one concealed text feature corresponding to the at least one text data block subsequent to the text data block to obtain at least one coarse fusion text feature. A fine fusion is performed on the at least one coarse fusion text feature to obtain a fine fusion text feature corresponding to the text data block. A length corresponding to the fine fusion text feature is regulated. The fine fusion text feature with the regulated length is transformed into the acoustic feature.