Huawei Technologies Co., Limited

Midea Group Co., Ltd.

Huawei Device Co., Ltd.

Honor Device Co., Ltd.

Seetatech (beijing) Technology Co., Ltd.

Junyang Zhou

Junbin Liang

Hongrui Jiang

Xiyong Wang

Weijun Zheng

Dongyan Wang

Xiaochun Li

Xiujuan Chai

Linnan Zhu

Zhuang Liu

Nanjun Li

Shiguang Shan

Zixuan Yang

Xilin Chen

Dahai Yu

Haishi Lin

Jianming Wu

Yongwen Long

Qunhu Xiao

Bidong Chen

Zongxu Zhou

Embodiments of the present invention provide a speech recognition method and a terminal. The method includes: listening, by a speech wakeup apparatus, to speech information in a surrounding environment; when determining that the speech information obtained by listening matches a speech wakeup model, buffering, by the speech wakeup apparatus, speech information, of first preset duration, obtained by listening, and sending a trigger signal for triggering enabling of a speech recognition apparatus, where the trigger signal is used to instruct the speech recognition apparatus to read and recognize the speech information buffered by the speech wakeup apparatus; and recognizing first speech information buffered by the speech wakeup apparatus and the second speech information obtained by listening, to obtain a recognition result.

Speech recognition method, speech wakeup apparatus, speech recognition apparatus, and terminal

A method for quickly starting an application service, and a terminal. The method includes acquiring, by a terminal, event trigger information; starting, by the terminal, the application service software after determining that the event trigger information meets a preset quick startup condition; and acquiring, by the terminal, a voice instruction input by a user, and running the application service software according to the voice instruction. According to the method provided in the embodiments of the present disclosure, application service software is started by using event trigger information, so that a background of a terminal starts to perform recording only after the application service software is started, and background recording is stopped after the terminal provides an application service for a user, preventing a recording device in the background of the terminal from being always in a recording state, and further reducing power consumption of the terminal.

Method for quickly starting application service, and terminal

A rice cooker assembly uses machine learning models to identify and classify content in grain mixtures thereby to provide better automation of the cooking process. As one example, a rice cooker has a chamber storing grains. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model determines whether the contents of the chamber includes one type or multiple types of grain or whether the contents of the chamber includes any inedible objects. The machine learning model further classifies the one or more types of grains and inedible objects if any. The cooking process may be controlled accordingly. The machine learning model may be resident in the rice cooker or it may be accessed via a network.

Method of smart rice cookers capable of mixed grain cooking and abnormal conditions detection

A method for voice control on a microwave oven, and a microwave oven. The method for voice control on a microwave oven comprises a step of receiving a voice instruction input by a user (S), a step of checking whether default menu parameters corresponding to the voice instruction are pre-stored (S), a step of controlling the operation of a microwave oven according to the default menu parameters (S), and a step of controlling, when receiving a parameter change instruction, the operation of the microwave oven according to changed parameters, and updating the default menu parameters (S). The microwave oven comprises a receive module (), a check module (), a control module () and an update module (). The parameters of the microwave oven can be changed to meet different requirements.

Method for voice control on microwave oven, and microwave oven

Embodiments provide a process to identify one or more areas containing a hand or hands of one or more subjects in an image. The detection process can start with coarsely locating one or more segments in the image that contain portions of the hand(s) of the subject(s) in the image using a coarse CNN. The detection process can then combine these segments to obtain the one or more areas capturing the hand(s) of the subject(s) in the image. The combined area(s) can then be fed to a grid-based deep neural network finely detect area(s) in the image that contain only the hand(s) of the subject(s) captured.

Coarse-to-fine hand detection method using deep neural network

A speech recognition method, a speech recognition device, and an electronic device. In this method, first determining is performed by using a sample environment corresponding to a detection speech and a previous environment type, so as to output a corresponding speech correction instruction to a speech engine; then, a to-be-recognized speech is input to the speech engine and a noise type detection engine at the same time, and the speech engine corrects the to-be-recognized speech by using the speech correction instruction, so that quality of an original speech is not impaired by noise processing, and a corresponding initial recognition result is output; the noise type detection engine determines a current environment type by using the to-be-recognized speech and a speech training sample under a different environment; finally, confidence of the initial recognition result is adjusted by using the current environment type.

Environment adaptive speech recognition method and device

Embodiments of the present invention provide a voice identification method, which includes: obtaining voice data; obtaining a confidence value according to the voice data; obtaining a noise scenario according to the voice data; obtaining a confidence threshold corresponding to the noise scenario; and if the confidence value is greater than or equal to the confidence threshold, processing the voice data. An apparatus is also provided. The method and apparatus that flexibly adjust the confidence threshold according to the noise scenario greatly improve a voice identification rate under a noise environment.

Voice identification method and apparatus

Embodiments of the present invention provide a voice identification method, including: obtaining voice data; obtaining a first confidence value according to the voice data; obtaining a noise scenario according to the voice data; obtaining a second confidence value corresponding to the noise scenario according to the first confidence value; and if the second confidence value is greater than or equal to a pre-stored confidence threshold, processing the voice data. An apparatus is also provided. The method and apparatus that flexibly adjust the confidence value according to the noise scenario greatly improve a voice identification rate under a noise environment.

An information recognition method and apparatus are provided. The method includes receiving, by a terminal, voice information, extracting a voice feature from the voice information, performing matching calculation on the voice feature and a phoneme string corresponding to each candidate text in multiple candidate texts to obtain a recognition result, where the recognition result includes at least one command word and a label corresponding to the at least one command word, and recognizing, according to the label corresponding to the at least one command word, an operation instruction corresponding to the voice information. A terminal recognizes text information, which is corresponding to voice information input by a user, as an operation instruction.

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Shenzhen, China

Junyang Zhou