Convolutional long-and-short-term-memory end-to-end deep neural network for voice cheating detection

Abstract

The invention is a convolutional long-and-short-term-memory end-to-end deep neural network for voice cheating detection. The deep neural network comprises a convolutional neural network front end with long-and-short-term memory sequence mapping and a neural network direct classifier. The convolutional neural network front end includes at least two convolutional neural networks (CNN) for standard feature extraction and at least one CNN for label prediction; and the CNN for label prediction carries out label prediction according to a long-and-short-term memory sequence way. According to the invention, comprehensive capacities of all existing features are examined, so that the feature extraction process is avoided; and adaptability to different tasks by the method architecture is also improved.

Description

technical field [0001] The invention relates to a technology in the field of speech processing, in particular to a convolutional long-short-term memory-based end-to-end deep neural network (CLDNN) for speech deception detection with original wave input. Background technique [0002] Spoof detection is a branch of speaker verification that distinguishes between real (human) and artificial (deceptive) spoken utterances. The main purpose of deception detection is to compute a score for each utterance and use the score to distinguish between these two (deceptive, human) utterance categories. The score is used to calculate a threshold by which an utterance can be classified as genuine (if its score is greater than the defined threshold) or deceptive (if its score is below the threshold). [0003] Detecting deceptive speech requires features: artificial vectors aiming to represent a given utterance in a lower dimensional space, uniqueness being paramount. In traditional speech-r...

AI Technical Summary

Problems solved by technology

[0009] In view of the defects in the prior art that the features in the prior art can only be extracted outside the task, and are not the optimal features that can reflect the spoofing task, and the matching degree between the terminal classifier and the detected features is not high, a method for voice spoofing is proposed. The end-to-end deep neural network based on convolutional long-term short-term memory for detection avoids the process of feature extraction and improves the adaptability of the method architecture for different tasks by considering the comprehensive capabilities of various existing features

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