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

Beijing Baidu Netcom Science Technology Co., Ltd

Bing Jiang

Xiangang Li

Chao Li

Tianyu Zhang

Jun Huang

Zhenan Li

Xiaokong Ma

Ke Ding

Xiliang Deng

Houkai Liu

A speech recognition and processing method, a device and a non-volatile computer storage medium are provided. The method comprises: performing training on a pre-set processing model according to speech sample data from all areas of a country to generate a general acoustic model for common speech (S); and performing self-adaptive training on the general acoustic model according to speech sample data of respective province to generate an acoustic model for common speech with dialectal accent corresponding to the respective province (S).

Voice recognition processing method, device and computer storage medium

Disclosed are a speech recognition method, a device and a computer readable storage medium. The speech recognition method comprises: performing acoustic characteristic extraction on an input voice, so as to obtain an acoustic characteristic (S); acquiring an acoustic model, a parameter of the acoustic model being a binarization parameter (S); and according to the acoustic characteristic and the acoustic model, performing speech recognition (S).

Voice recognition method, device and computer storage medium

A method and apparatus of training an acoustic feature extracting model, a device and a computer storage medium. The method comprises: considering a first acoustic feature extracted respectively from speech data corresponding to user identifiers as training data; training an initial model based on a deep neural network based on a criterion of a minimum classification error, until a preset first stop condition is reached; using a triplet loss layer to replace a Softmax layer in the initial model to constitute an acoustic feature extracting model, and continuing to train the acoustic feature extracting model until a preset second stop condition is reached, the acoustic feature extracting model being used to output a second acoustic feature of the speech data; wherein the triplet loss layer is used to maximize similarity between the second acoustic features of the same user, and minimize similarity between the second acoustic features of different users.

Method and apparatus of training acoustic feature extracting model, device and computer storage medium

Embodiments of the present disclosure disclose a method and apparatus for switching multiple speech recognition models. The method includes: acquiring at least one piece of speech information in user input speech; recognizing the speech information and matching a linguistic category for the speech information to determine a corresponding target linguistic category based on a matching degree; and switching a currently used speech recognition model to a speech recognition model corresponding to the target linguistic category. The embodiments of the present disclosure determine the corresponding target linguistic category based on the matching degree by recognizing the speech information and matching the linguistic category for the speech information, and switch the currently used speech recognition model to the speech recognition model corresponding to the target linguistic category.

Multiple voice recognition model switching method and apparatus, and storage medium

A speaker adaption method and a speaker adaption apparatus, a device and a storage medium are provided. The method includes: acquiring first speech data of a target speaker; inputting the first speech data to a pre-trained batch normalization (BN) network to be subjected to an adaptive training to acquire a speech recognition model including a speech parameter of the target speaker.

Speaker adaption method and apparatus, and storage medium

A method and apparatus of building an acoustic feature extracting model, and an acoustic feature extracting method and apparatus. The method of building an acoustic feature extracting model comprises: considering first acoustic features extracted respectively from speech data corresponding to user identifiers as training data; using the training data to train a deep neural network to obtain an acoustic feature extracting model; wherein a target of training the deep neural network is to maximize similarity between the same user's second acoustic features and minimize similarity between different users' second acoustic features. The acoustic feature extracting model according to the present disclosure can self-learn optimal acoustic features that achieves a training target. As compared with a conventional acoustic feature extracting manner with a preset feature type and transformation manner, the acoustic feature extracting manner of the present disclosure achieves better flexibility and higher accuracy.

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Beijing, China

Bing Jiang