基于生成对抗网络的噪声环境下鲁棒TTS训练框架

Kishore Kumar Botsa, Lithin Reddy Marla, S. Gangashetty
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引用次数: 0

摘要

人类通过动态改变其声音特征来克服背景噪声对语音的退化效应,这是在干净语音上训练的文本到语音系统无法做到的,从而导致可理解性下降。为了提高系统的可理解性,需要大量的语音样本,在噪声背景和信噪比等各种条件下难以采集。本文提出了一种基于生成对抗网络训练框架的文本到语音的噪声依赖增强方法,以在噪声中生成可理解的语音。合成器网络的学习机制灵感来自于人类用来消除各种背景噪声影响的声学反馈。这样训练的系统在自助餐厅噪声条件下用两个客观指标进行评估,这表明与在3个信噪比的干净语音上训练的模型相比,可理解性有所提高。所提出的修改不需要任何额外的训练数据,可以应用于各种采用反向传播算法进行训练的深度神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generative Adversarial Network based Training Framework for Robust TTS in Noisy Environment
Humans overcome the degradation effect of background noise on the speech by changing their vocal characteristics dynamically, which text-to-speech systems trained on clean speech cannot, resulting in degraded intelligibility. To improve the intelligibility of such system requires a large amount of speech samples, which is difficult to collect for various conditions like noise backgrounds and signal-to-noise ratios. This paper presents a noise dependent enhancement to text-to-speech, based on generative adversarial network training framework to generate intelligible speech in noise. The learning mechanism for the synthesizer network is inspired from the acoustic feedback humans use to nullify the effect of various background noises. The system thus trained is evaluated under cafeteria noise condition with two objective measures, which indicated improvement in intelligibility compared to models trained on clean speech across 3 SNRs. The proposed modification does not require any additional training data and can be applied to a variety of Deep Neural Networks that employ back-propagation algorithm for training.
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