Design of a Real-Time GAN based Speech Recognizer for Consumer Electronics

Pubali Roy, Pranav M Bidare, P. Bharadwaj, M. J
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Abstract

Modern consumer electronics including automotive electronics, televisions, microwave ovens, music systems, refrigerators with speech controlled features and hands-free operation have spearheaded research in designing smart electronic devices for consumers. Real-time speech recognizer is the main module for these systems and a lot of research is in progress with the design of real-time speech recognizers with a quicker recognition time being considered as one of the challenges. Generative Adversarial Networks (GAN) are mainly used with two dimensional signals such as image for applications such as recognition, synthesis, translation etc. In this paper, an attempt is made to design and evaluate a real-time GAN based pattern recognizer for one-dimensional speech signal. In order to achieve this, the one-dimensional speech signal is first converted into a two dimensional spectrogram and fed to the GAN model for recognition. The proposed speech recognizer yielded a maximum recognition accuracy of 100% with a recognition time of 49.10ms per word. The proposed work can be easily employed to design various smart consumer electronics.
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消费类电子产品实时GAN语音识别器的设计
现代消费电子产品,包括汽车电子、电视、微波炉、音乐系统、具有语音控制功能和免提操作的冰箱,在为消费者设计智能电子设备的研究中处于领先地位。实时语音识别器是这些系统的主要模块,实时语音识别器的设计是当前研究的热点之一,快速识别时间是实时语音识别器设计的难点之一。生成对抗网络(GAN)主要用于图像等二维信号的识别、合成、翻译等应用。本文尝试设计并评估一种基于GAN的一维语音信号实时模式识别器。为了实现这一目标,首先将一维语音信号转换为二维频谱图,并将其输入GAN模型进行识别。该语音识别器的最大识别准确率为100%,每个单词的识别时间为49.10ms。所提出的工作可以很容易地用于设计各种智能消费电子产品。
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