Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Feng Rao

Li Hui Lu

Xiang Zhang

Bo Song Chen

Shuai Yue

Eryu Wang

Haibo Liu

Lou Li

Duling Lu

Lu Li

Jianxiong Ma

Hailong Liu

Jie Hou

Dadong Xie

Qiang Cheng

Linghui Kong

Bo Chen

Tao Li

Jian Ding

Fei Wang

Jian Hua Liu

Xiao Liu

Song Liu

Pengfei Xiong

Jun Qiu

Yang Mo

Shang Yu

Minhui Wu

Xiaobo Zhou

Ping Zheng

Zongyao Tang

Lu Ll

A speech recognition method is provided. The method includes: obtaining a voice signal; processing the voice signal according to a speech recognition algorithm to obtain n candidate recognition results, the candidate recognition results including text information corresponding to the voice signal; identifying a target result from among the n candidate recognition results according to a selection rule selected from among m selection rules, the selection rule having an execution sequence of j, the target result being a candidate recognition result that has a highest matching degree with the voice signal in the n candidate recognition results, an initial value of j being 1; and identifying the target result from among the n candidate recognition results according to a selection rule having an execution sequence of j+1 based on the target result not being identified according to the selection rule having the execution sequence of j.

Speech recognition method and apparatus, and storage medium

A method is performed at a computer for automatically correcting typographical errors. The computer selects a target word in a target sentence and identifies a target word therein as having a typographical error and first and second sequences of words separated by the target word as context. After identifying, among a database of grammatically correct sentences, a set of sentences having the first and second sequences of words, each sentence including a replacement word, the computer selects a set of candidate grammatically correct sentences whose corresponding replacement words have similarities to the target word above a pre-set threshold, Finally, the computer chooses, among the set of candidate grammatically correct sentences, a fittest grammatically correct sentence according to a linguistic model and replaces the target word in the target sentence with the replacement word within the fittest grammatically correct sentence.

Model-based automatic correction of typographical errors

The present application discloses a method, an electronic system and a non-transitory computer readable storage medium for recognizing audio commands in an electronic device. The electronic device obtains audio data based on an audio signal provided by a user and extracts characteristic audio fingerprint features from the audio data. The electronic device further determines whether the corresponding audio signal is generated by an authorized user by comparing the characteristic audio fingerprint features with an audio fingerprint model for the authorized user and with a universal background model that represents user-independent audio fingerprint features, respectively. When the corresponding audio signal is generated by the authorized user of the electronic device, an audio command is extracted from the audio data, and an operation is performed according to the audio command.

Systems and methods for audio command recognition with speaker authentication

A method is performed at a device having one or more processors and memory. The device establishes a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data. The device establishes a second-level DNN model by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, wherein the second-level DNN model specifies a plurality of high-level voiceprint features. Using the second-level DNN model, registers a first high-level voiceprint feature sequence for a user based on a registration speech sample received from the user. The device performs speaker verification for the user based on the first high-level voiceprint feature sequence registered for the user.

Method and device for voiceprint recognition

The present invention, pertaining to the field of speech recognition, discloses a reminder setting method and apparatus. The method includes: acquiring speech signals; acquiring time information in speech signals by using keyword recognition, and determining reminder time for reminder setting according to the time information; acquiring text sequence corresponding to the speech signals by using continuous speech recognition, and determining reminder content for reminder setting according to the time information and the text sequence; and setting a reminder according to the reminder time and the reminder content. According to the present invention, acquiring time information in speech signals by using keyword recognition ensures correctness of time information extraction, and achieves an effect that correct time information is still acquired by keyword recognition to set a reminder even in the case that a recognized text sequence is incorrect due to poor precision in whole text recognition in the speech recognition.

Reminder setting method and apparatus

An automatic speech recognition method includes at a computer having one or more processors and memory for storing one or more programs to be executed by the processors, obtaining a plurality of speech corpus categories through classifying and calculating raw speech corpus; obtaining a plurality of classified language models that respectively correspond to the plurality of speech corpus categories through a language model training applied on each speech corpus category; obtaining an interpolation language model through implementing a weighted interpolation on each classified language model and merging the interpolated plurality of classified language models; constructing a decoding resource in accordance with an acoustic model and the interpolation language model; and decoding input speech using the decoding resource, and outputting a character string with a highest probability as a recognition result of the input speech.

Method and system for building a topic specific language model for use in automatic speech recognition

Systems and methods are provided for speech recognition. For example, audio characteristics are extracted from acquired voice signals; a syllable confusion network is identified based on at least information associated with the audio characteristics; a word lattice is generated based on at least information associated with the syllable confusion network and a predetermined phonetic dictionary; and an optimal character sequence is calculated in the word lattice as a speech recognition result.

Systems and methods for speech recognition

A method and a device for training a DNN model includes: at a device including one or more processors and memory: establishing an initial DNN model; dividing a training data corpus into a plurality of disjoint data subsets; for each of the plurality of disjoint data subsets, providing the data subset to a respective training processing unit of a plurality of training processing units operating in parallel, wherein the respective training processing unit applies a Stochastic Gradient Descent (SGD) process to update the initial DNN model to generate a respective DNN sub-model based on the data subset; and merging the respective DNN sub-models generated by the plurality of training processing units to obtain an intermediate DNN model, wherein the intermediate DNN model is established as either the initial DNN model for a next training iteration or a final DNN model in accordance with a preset convergence condition.

Method and device for parallel processing in model training

A method and device for voiceprint recognition, include: establishing a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data; obtaining a plurality of high-level voiceprint features by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, and the tuning producing a second-level DNN model specifying the plurality of high-level voiceprint features; based on the second-level DNN model, registering a respective high-level voiceprint feature sequence for a user based on a registration speech sample received from the user; and performing speaker verification for the user based on the respective high-level voiceprint feature sequence registered for the user.

A method of recognizing speech is provided that includes generating a decoding network that includes a primary sub-network and a classification sub-network. The primary sub-network includes a classification node corresponding to the classification sub-network. The classification sub-network corresponds to a group of uncommon words. A speech input is received and decoded by instantiating a token in the primary sub-network and passing the token through the primary network. When the token reaches the classification node, the method includes transferring the token to the classification sub-network and passing the token through the classification sub-network. When the token reaches an accept node of the classification sub-network, the method includes returning a result of the token passing through the classification sub-network to the primary sub-network. The result includes one or more words in the group of uncommon words. A string corresponding to the speech input is output that includes the one or more words.

Method and system for automatic speech recognition

An electronic device with one or more processors and memory trains an acoustic model with an international phonetic alphabet (IPA) phoneme mapping collection and audio samples in different languages, where the acoustic model includes: a foreground model; and a background model. The device generates a phone decoder based on the trained acoustic model. The device collects keyword audio samples, decodes the keyword audio samples with the phone decoder to generate phoneme sequence candidates, and selects a keyword phoneme sequence from the phoneme sequence candidates. After obtaining the keyword phoneme sequence, the device detects one or more keywords in an input audio signal with the trained acoustic model, including: matching phonemic keyword portions of the input audio signal with phonemes in the keyword phoneme sequence with the foreground model; and filtering out phonemic non-keyword portions of the input audio signal with the background model.

Keyword detection with international phonetic alphabet by foreground model and background model

A method, system and computer storage medium for visual searching based on cloud service is disclosed. The method includes: receiving, from a client, an image recognition request of cloud service, the request containing image data; forwarding, according to a set classified forwarding rule, the image data to a corresponding classified visual search service; recognizing, by the respective corresponding classified visual search services, corresponding classified type information in the image data, and determining a corresponding name of the image data in accordance with the respective classified type information, and obtaining a classified visual search result; summarizing and sending, to a client, the classified visual search result of the corresponding classified visual search service. By detection and recognition of the classified type information of the image data, the comprehensive feature information of a picture is obtained, based on which further applications are allowed, and thus the user experience is improved.

Method, system and computer storage medium for visual searching based on cloud service

A method includes: acquiring data samples; performing categorized sentence mining in the acquired data samples to obtain categorized training samples for multiple categories; building a text classifier based on the categorized training samples; classifying the data samples using the text classifier to obtain a class vocabulary and a corpus for each category; mining the corpus for each category according to the class vocabulary for the category to obtain a respective set of high-frequency language templates; training on the templates for each category to obtain a template-based language model for the category; training on the corpus for each category to obtain a class-based language model for the category; training on the class vocabulary for each category to obtain a lexicon-based language model for the category; building a speech decoder according to an acoustic model, the class-based language model and the lexicon-based language model for any given field, and the data samples.

Method and apparatus for building a language model

A method and a device for training an acoustic language model, include: conducting word segmentation for training samples in a training corpus using an initial language model containing no word class labels, to obtain initial word segmentation data containing no word class labels; performing word class replacement for the initial word segmentation data containing no word class labels, to obtain first word segmentation data containing word class labels; using the first word segmentation data containing word class labels to train a first language model containing word class labels; using the first language model containing word class labels to conduct word segmentation for the training samples in the training corpus, to obtain second word segmentation data containing word class labels; and in accordance with the second word segmentation data meeting one or more predetermined criteria, using the second word segmentation data containing word class labels to train the acoustic language model.

Method and device for acoustic language model training

A method and an apparatus are provided for retrieving keyword. The apparatus configures at least two types of language models in a model file, where each type of language model includes a recognition model and a corresponding decoding model; the apparatus extracts a speech feature from the to-be-processed speech data; performs language matching on the extracted speech feature by using recognition models in the model file one by one, and determines a recognition model based on a language matching rate; and determines a decoding model corresponding to the recognition model; decoding the extracted speech feature by using the determined decoding model, and obtains a word recognition result after the decoding; and matches a keyword in a keyword dictionary and the word recognition result, and outputs a matched keyword.

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Shenzhen, China

Feng Rao