Keyword search method based on unified representation

A keyword and characterization technology, applied in audio data retrieval, metadata audio data retrieval, special data processing applications, etc., can solve the problems of modeling, use, and maintenance inconvenience, and achieve the effect of convenient modeling

Active Publication Date: 2020-01-31
TSINGHUA UNIV
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AI Technical Summary

Problems solved by technology

[0003] At present, the text-based keyword retrieval system and the sample-based keyword retrieval system adopt completely different architectures, and they

Method used

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  • Keyword search method based on unified representation

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Embodiment Construction

[0018] The preferred embodiments will be described in detail below in conjunction with the accompanying drawings.

[0019] Step 1: Use a large amount of speech data to train a neural network speech autoencoder with a bottleneck layer: the training data can be mixed data of various languages, and no content labeling is required; the input of the autoencoder is a speech (or feature), and the output It is the same segment of speech (or features); the neural network can adopt deep neural network, convolutional neural network or recursive neural network, the middle is a bottleneck layer with a small number of nodes, and the input and output are symmetrical structures; the training goal is to make The mean square error between the output speech (or feature) and the input speech (or feature) is the smallest;

[0020] Step 2: Use the bottleneck layer of the neural network speech autoencoder as the output layer to obtain the acoustic representation vector extractor: keep the part from ...

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Abstract

The invention belongs to the technical field of voice signal processing, and in particular relates to a keyword search method based on unified representation. The method comprises the following steps:training a neural network voice auto-encoder with a bottleneck layer by adopting abundant voice data, thus obtaining an acoustics representation vector extractor; training a neural network text auto-encoder with a bottleneck layer by adopting abundant text data, thus obtaining a language representation vector extractor; extracting corresponding acoustics representation vectors and language representation vectors respectively by adopting abundant voice data segments and corresponding text data segments for training a unified vector extractor; acquiring inquiring vectors of a text keyword through the language representation vector extractor and the unified vector extractor; acquiring inquiring vectors of a voice keyword through the acoustics representation vector extractor and the unified vector extractor; and for to-be-inquired voice, acquiring a plurality of index vectors in segments in the sequence through the acoustics representation vector extractor and the unified vector extractor, and calculating the distances among the inquiring vectors, wherein if the value is smaller than a preset threshold, considering that the keyword is hit.

Description

technical field [0001] The invention belongs to the technical field of speech signal processing, in particular to a keyword retrieval method based on unified representation. Background technique [0002] Speech keyword retrieval is one of the important core technologies in the field of speech signal processing. According to different user query inputs, keyword retrieval can be divided into two categories: text-based keyword retrieval and example-based keyword retrieval. Text-based keyword retrieval keywords are given in the form of text, with the help of ASR (Automatic Speech Recognition) technology, and then search and match the text according to the recognition results; sample-based keyword retrieval keywords are based on speech fragments (sample) Given in the form of , the acoustic features are generally used directly for template matching of time series. [0003] At present, the text-based keyword retrieval system and the sample-based keyword retrieval system adopt com...

Claims

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Application Information

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IPC IPC(8): G10L15/08G10L15/02G10L15/26G10L19/00G10L19/038G10L25/30G06F16/68
CPCG10L15/08G10L15/02G10L19/00G10L19/038G10L25/30G06F16/686G10L2015/088G10L2019/0005G10L15/26
Inventor 张卫强
Owner TSINGHUA UNIV
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