基于受限玻尔兹曼机的语音带宽扩展

王迎雪; 赵胜辉; 于莹莹; 匡镜明

doi:10.11999/JEIT151034

基于受限玻尔兹曼机的语音带宽扩展

doi: 10.11999/JEIT151034

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出版历程
- 收稿日期: 2015-09-14
- 修回日期: 2016-03-03
- 刊出日期: 2016-07-19

Speech Bandwidth Extension Based on Restricted Boltzmann Machines

摘要

摘要: 语音带宽扩展是为了提高语音质量，利用语音低频和高频之间的相关性重构语音高频的一种技术。高斯混合模型法是语音带宽技术中被广泛应用的一种方法，但是，由于该方法假设语音高频、低频服从高斯分布，且只表征了语音低频、高频之间的线性关系，从而导致合成的高频语音出现失真。因此，该文提出一种基于受限玻尔兹曼机的方法，该方法利用两个高斯伯努利受限玻尔兹曼机提取语音低频和高频中蕴含的高阶统计特性；并利用前馈神经网络将语音低频高阶统计特性参数映射为高频高阶统计特性参数。这样，通过提取语音低频和高频中蕴含的高阶统计特性，该方法可以深层挖掘语音高频和语音低频之间的实际关系，从而更加准确地模拟频谱包络分布，合成质量更高的语音。客观测试、主观测试结果表明，该方法性能优于传统的高斯混合模型方法。
- 语音带宽扩展 /
- 受限玻尔兹曼机 /
- 前馈神经网络 /
- 高斯混合模型
Abstract: Speech Bandwidth Extension (BWE) is a technique that attempts to improve the speech quality by recovering the missing High Frequency (HF) components using the correlation that exists between the Low Frequency (LF) and HF parts of the wide-band speech signal. The Gaussian Mixture Model (GMM) based methods are widely used, but it recovers the missing HF components on the assumption that the LF and HF parts obey a Gaussian distribution and gives their linear relationship, leading to the distortion of reconstructed speech. This Study proposes a new speech BWE method, which uses two Gaussian-Bernoulli Restricted Boltzmann Machines (GBRBMs) to extract the high-order statistical characteristics of spectral envelopes of the LF and HF respectively. Then, high-order features of the LF are mapped to those of the HF using a Feedforward Neural Network (FNN). The proposed method learns deep relationship between the spectral envelopes of LF and HF and can model the distribution of spectral envelopes more precisely by extracting the high-order statistical characteristics of the LF components and the HF components. The objective and subjective test results show that the proposed method outperforms the conventional GMM based method.
- Speech bandwidth extension /
- Restricted Boltzmann machines /
- Feedforward Neural Networks (FNN) /
- Gaussian mixture model

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