Abstract: Concerning the problem that the existing Voice Activity Detection (VAD) algorithms based on Hidden Markov Model (HMM) were poor to track noise, a method using Baum-Welch algorithm was proposed to train the noise with different characteristics, and the corresponding noise model was generated to establish a library. When voice activity was detected, depending on the measured background noise of the speech, the voice was dynamically matched to a noise model in the library. Meanwhile, in order to meet real-time requirements of speech signal processing, reduce the complexity of the speech parameter extraction, the threshold was improved to ensure the inter-frame correlation of the speech signal. Under different noise environments, the improved algorithm performance was tested and compared with Adaptive Multi-Rate (AMR), G.729B of the International Telecommunications Union (ITU-T). The test results show that the improved algorithm can effectively improve the accuracy of detection and noise tracking ability in real-time voice signal processing.

Key words: Hidden Markov Model (HMM), voice activity detection, Baum-Welch algorithm, noise library, threshold

摘要： 针对现有基于隐马尔可夫模型（HMM）的语音激活检测（VAD）算法对噪声的跟踪性能不佳的问题，提出采用Baum-Welch算法对具有不同特性的噪声进行训练，并生成相应噪声模型，建立噪声库的方法。在语音激活检测时，根据待测语音背景噪声的不同，动态地匹配噪声库中的噪声模型；同时，为了适应语音信号的实时处理，降低了语音参数提取的复杂度，并对判决阈值提出改进，以保证语音信号帧间的相关性。在不同噪声环境下对改进算法进行性能测试并与自适应多速率编码（AMR）标准、国际电信联盟电信标准分局（ITU-T）的G.729B标准比较，测试结果表明，改进算法在实时语音信号处理中能够有效提高检测的准确率及噪声跟踪能力。

关键词: 隐马尔可夫模型, 语音激活检测, Baum-Welch算法, 噪声库, 阈值