Baidu Online Network Technology (Beijing) Co., Ltd.

Jue Sun

Xiangang Li

Chao Li

Mingming Chen

Embodiments of the present disclosure provide a speech recognition method based on artificial intelligence, and a terminal. The method includes obtaining speech data to be recognized; performing a processing on the speech data to be recognized using a trained sub-band energy normalized acoustic model, to determine an normalized energy feature corresponding to each time-frequency unit in the speech data to be recognized; and determining text data corresponding to the speech data to be recognized according to the normalized energy feature corresponding to each time-frequency unit.

Speech recognition method based on artificial intelligence and terminal

Embodiments of the present disclosure provide a method and a device for waking up via a speech based on AI and a computer device. The method includes the followings. A windowing and framing operation is performed on an online recorded speech, to obtain at least one speech frame. A feature extraction is performed on the at least one speech frame, to obtain speech features. A calculation is performed on a static speech feature contained in the at least one speech frame through a speech wake-up model based on convolutional neural network, to obtain a first posteriori probability matched with a category of non-wake-up words and a second posteriori probability matched with a category of wake-up words. It is determined that a wake-up word is contained in the online recorded speech, when the second posteriori probability is greater than or equal to a preset threshold.

Method and device for waking up via speech based on artificial intelligence and computer device

A method and an apparatus for classifying a telephone dialing test audio based on AI is provided. Data of a telephone dialing test audio is acquired first, then the data of the telephone dialing test audio is processed via a preset classifier so as to obtain similarities among the data of the telephone dialing test audio and telephone types in the preset classifier, in which the preset classifier is a deep-learning model determined by historical data of telephone dialing test audios and their corresponding telephone types; finally, the telephone type corresponding to the telephone dialing test audio is determined according to the similarities. With the method and apparatus, the telephone dialing test audio is classified via machine learning to determine whether a user is a normal user, thus human costs is saved and an efficiency of dialing test is increased.

Method and apparatus for classifying telephone dialing test audio based on artificial intelligence

A method and a device for searching according to a speech based on artificial intelligence are provided. The method includes: identifying an input speech of a user to determine whether the input speech is a child speech; filtrating a searched result obtained according to the input speech to obtain a filtrated searched result, if the input speech is the child speech; and feeding the filtrated searched result back to the user.

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Beijing, China

Jue Sun