语音增强技术及其实现

Jahnavi Nandeti, Ravikumar Kandagatla, Ragipati Naga Sai Tejaswini, Mamidi Krupakar, Paragati Haveela
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引用次数: 0

摘要

语音增强的目的是提高语音质量和可理解性。质量指的是语音中无噪音的数量,可理解性指的是句子中被理解的单词数量的百分比。语音增强中噪声估计是一个重要的环节。本文讨论了噪声估计方法及其在MATLAB中的实现。基本噪声估计包括从第一帧估计噪声,然后讨论基于语音存在和缺席的方法。基于统计的语音增强方法在语音增强中也发挥着重要作用。本文讨论了传统的统计估计方法及其在MATLAB中的实现。在这项工作中,讨论了不同的数据库(干净的,有噪声的语音样本)可用于语音增强。为了评估语音增强算法,讨论了文献中可用的客观和主观性能指标,并讨论了MATLAB实现的来源。通过信噪比(SNR)、分段信噪比(Seg SNR)、语音质量感知评价(PESQ)对语音增强方法的性能进行了比较。并利用谱图分析了其主观性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speech Enhancement Techniques and its Implementation
Speech enhancement aims to improve quality and intelligibility. Quality refers to the amount of noise free in speech and intelligibility refers to the percentage number of words understand in the sentence. Speech enhancement involves noise estimation as crucial part. Noise estimation approaches and their implementation using MATLAB is discussed in this work. Basic noise estimation includes estimation of noise from first frames and then speech presence and absence based approaches are discussed. Also the statistical based approaches for speech enhancement plays important role in speech enhancement. In this work traditional statistical estimation methods and their implementation using MATLAB is discussed. In this work different databases (clean, noisy speech samples) available for speech enhancement are discussed. To evaluate the speech enhancement algorithms the objective and subjective performance measures available in literature are discussed and the sources for MATLAB implementation is discussed. The performance of speech enhancement methods is compared with help of Signal to Noise Ratio (SNR), Segmental SNR (Seg SNR), Perceptual Evaluation of Speech Quality (PESQ). Also the subjective performance is analyzed using spectrograms.
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