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

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Jinfeng Bai

Lei Jia

Xu Chen

Ce Zhang

Zhijian Wang

Cong Gao

Xin Li

Sheng Qian

Zhen Wu

Bin Huang

Saisai Zou

Xiaokong Ma

Xingyuan Peng

Chuanlei Zhai

Tao Chen

Zhijie Chen

Xiaoyin Fu

Mingxin Liang

Guibin Wang

Yufang Wu

Hua Liang

Runqiang Han

Jiaxiang Ge

Zhipeng Chen

The present disclosure provides a speech processing method, and a method for generating a speech processing model, related to a field of signal processing technologies. The speech processing method includes: obtaining M speech signals to be processed and N reference signals; performing sub-band decomposition on each of the M speech signals and each of the N reference signals to obtain frequency-band components in each speech signal and each reference signal; processing the frequency-band components in each speech signal and each reference signal by using an echo cancellation model, to obtain an ideal ratio mask corresponding to the N reference signals in each frequency band of each speech signal; and performing echo cancellation on each frequency-band component of each speech signal based on the ideal ratio mask corresponding to the N reference signals in each frequency band of each speech signal, to obtain M echo-cancelled speech signals.

Speech processing method and method for generating speech processing model

The present disclosure provides a method and a device for processing voice interaction, an electronic device and a storage medium. The method includes: determining a first integrity of a voice instruction from a user by using a pre-trained integrity detection model in response to detecting that the voice instruction from the user is not a high-frequency instruction; determining a waiting duration for the voice instruction based on the first integrity and a preset integrity threshold, wherein the waiting duration for the voice instruction indicates a length of period between a time when a voice interaction device determines that receiving the voice instruction is completed and a time when the voice interaction device performs an operation in response to the voice instruction of the user; and controlling the voice interaction device to respond to the voice instruction of the user based on the waiting duration.

Method and device for processing voice interaction, electronic device and storage medium

Embodiments of the present disclosure relate to a method and an apparatus for speech interaction, and a computer readable storage medium. The method may include determining text information corresponding to a received speech signal. The method also includes obtaining label information of the text information by labeling elements in the text information. In addition, the method further includes determining first intention information of the text information based on the label information. The method further includes determining a semantic of the text information based on the first intention information and the label information.

Method and apparatus for speech interaction, and computer storage medium

The present disclosure discloses a control method and a control apparatus for speech interaction. The detailed implementation solution of the control method for the speech interaction includes: collecting an audio signal; detecting a wake-up word in the audio signal to obtain a wake-up word result; and playing a prompt tone and/or executing a speech instruction in the audio signal based on the wake-up word result.

Control method and control apparatus for speech interaction, storage medium and system

A method and an apparatus for recognizing a voice are provided. The method may include: inputting a target voice into a pre-trained voice recognition model to obtain an initial text output by at least one recognition network in the voice recognition model, the recognition network including a plurality of preset types of processing layers, and at least one type of processing layer of the recognition network being obtained by training based on a voice sample in a preset direction interval; and determining a voice recognition result of the target voice, based on the initial text.

Method and apparatus for recognizing voice

The disclosure provides a speech recognition method, a device and a computer-readable storage medium. The method includes obtaining a first voice signal collected from a first microphone in a microphone array and a second voice signal collected from a second microphone in the microphone array, the microphone array including at least two microphones, such as two, three or six microphones. The method further includes extracting enhanced features associated with the first voice signal and the second voice signal through a neural network, and obtaining a speech recognition result based on the enhanced features extracted.

Speech recognition method, device, and computer-readable storage medium

Control method and control apparatus for speech interaction

The present disclosure discloses an interactive voice-control method and apparatus, a device and a medium. The method includes: obtaining a sound signal at a voice interaction device and recognized information that is recognized from the sound signal; determining an interaction confidence of the sound signal based at least on at least one of an acoustic feature representation of the sound signal and a semantic feature representation associated with the recognized information; determining a matching status between the recognized information and the sound signal; and providing the interaction confidence and the matching status for controlling a response of the voice interaction device to the sound signal.

Interactive voice-control method and apparatus, device and medium

The present application discloses a training method and an apparatus for a speech synthesis model, electronic device, and storage medium. The method includes: taking a syllable input sequence, a phoneme input sequence and a Chinese character input sequence of a current sample as inputs of an encoder of a model to be trained, to obtain encoded representations of these three sequences at an output end of the encoder; fusing the encoded representations of these three sequences, to obtain a weighted combination of these three sequences; taking the weighted combination as an input of an attention module, to obtain a weighted average of the weighted combination at each moment at an output end of the attention module; taking the weighted average as an input of a decoder of the model to be trained, to obtain a speech Mel spectrum of the current sample at an output end of the decoder.

Training method and apparatus for a speech synthesis model, and storage medium

According to embodiments of the present disclosure, a method, apparatus, device, and computer readable storage medium for voice interaction are provided. The method includes: determining a text corresponding to the voice signal based on a voice feature of a received voice signal. The method further includes: determining, based on the voice feature and the text, a matching degree between a reference voice feature of an element in the text and a target voice feature of the element. The method further includes: determining a first possibility that the voice signal is an executable command based on the text. The method further includes: determining a second possibility that the voice signal is the executable command based on the voice feature.

Method, apparatus, device and computer-readable storage medium for voice interaction

According to embodiments of the disclosure, a method and an apparatus for processing a speech signal, and a computer-readable storage medium are provided. The method includes obtaining a set of speech feature representations of a speech signal received. The method also includes generating a set of source text feature representations based on a text recognized from the speech signal, each source text feature representation corresponding to an element in the text. The method also includes generating a set of target text feature representations based on the set of speech feature representations and the set of source text feature representations. The method also includes determining a match degree between the set of target text feature representations and a set of reference text feature representations predefined for the text, the match degree indicating an accuracy of recognizing of the text.

Method, apparatus, and medium for processing speech signal

A method, apparatus, device, and storage medium for voice interaction. A specific embodiment of the method includes: extracting an acoustic feature from received voice data, the acoustic feature indicating a short-term amplitude spectrum characteristic of the voice data; applying the acoustic feature to a type recognition model to determine an intention type of the voice data, the intention type being one of an interaction intention type and a non-interaction intention type, and the type recognition model being constructed based on the acoustic feature of training voice data; and performing an interaction operation indicated by the voice data, based on determining that the intention type is the interaction intention type.

Method and apparatus for voice interaction, device and computer-readable storage medium

Methods, apparatuses, devices and computer-readable storage media for real-time speech recognition are provided. The method includes: based on an input speech signal, obtaining truncating information for truncating a sequence of features of the speech signal; based on the truncating information, truncating the sequence of features into a plurality of subsequences; and for each subsequence in the plurality of subsequences, obtaining a real-time recognition result through attention mechanism.

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Beijing, China

Jinfeng Bai