Advanced New Technologies Co., Ltd.

Zhejiang Tonghu Ashun Intelligent Technology Co., Ltd.

Alibaba Group Holding Limited

Peng Zhang

Xinkang Xu

Xinhui Hu

Jian Lu

Yun Chen

Di Zhang

Yi Zheng

Jianmin Pan

Zaixiang Chen

Boyu Yang

Minli Wu

The present disclosure discloses a method for synthesizing a speech. The method includes generating the speech based on a text with a speech synthesis model, wherein the speech synthesis model includes an embedding layer, a speech synthesis layer, and a position layer; and training the speech synthesis model when an evaluation index meets a preset condition, wherein the evaluation index includes one or more quality indexes determined based on at least a part of the text and at least a part of the speech.

Systems and methods for synthesizing speech

A method includes: receiving a service participation request of a target service transmitted by a user terminal, wherein the user terminal comprises an iOS operating system; obtaining target identification data from a system server according to the service participation request, wherein the target identification data comprises first identification data used for identifying whether the user terminal participates in the target service, and/or second identification data used for identifying whether the device data of the user terminal is modified, and the system server is a server corresponding to the iOS operating system; and according to the target identification data, determining whether to allow the user terminal to participate in the target service.

Service processing method and apparatus

A computer-implemented method includes obtaining, by one or more processing devices, a training sample dataset, wherein the training sample dataset comprises at least two pieces of sample data and a sample label corresponding to each piece of sample data; constructing, by the one or more processing devices, a first relational network of the at least two pieces of sample data, and representing the at least two pieces of sample data using nodes of the first relational network; obtaining, by the one or more processing devices, a vectorized representation of each node in the first relational network based on a predetermined network embedding learning model; and training, by the one or more processing devices, a classification model based on the training sample dataset, wherein the classification model associates the sample label with the vectorized representation of each node in the first relational network.

Classification model generation method and apparatus, and data identification method and apparatus

The disclosure provides methods and devices for object allocation. In one embodiment, the method comprises receiving, at a server, an object allocation message submitted by an allocation source via a communications application, the object allocation message including an identification of the allocation source, a number of to-be-allocated objects, and an object allocation time; extracting, by the server from an object set corresponding to the allocation source, objects corresponding to the number of to-be-allocated objects; controlling, by the server, acquisition times when the allocation destination will acquire the objects, so that the acquisition times do not happen before the object allocation time.

Methods and devices for controlling the timing of network object allocation in a communications network

Growing community of inventors

Hangzhou, China

Peng Zhang