Customized Speaker Verification System with Noise-Cancellation using Blind Source Separation

Tsung-Han Tsai, Ping-Cheng Hao, Fong-Lin Tsai
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Abstract

In this paper, a customized speaker verification system combined with noise-cancellation using blind source separation was proposed. This system is divided into two phases: the noise-cancellation phase and the speaker verification phase. In the noise-cancellation phase, a fast time-frequency mask technique based on Short Time Fourier Transform (STFT) was proposed for separating a mixture of two input sounds in a single signal. After obtaining the separated speech data, this input is processed to the wake-up word system. In the speaker verification phase, we use Mel-Frequency Cepstral Coefficients (MFCC) as the feature extraction module. Then we train the feature data into a voiceprint model and a state sequence model of the speaker using Gaussian mixture model (GMM) and hidden Markov model (HMM), respectively. An analysis is done on noisy speech signals corrupted by white noise at different angles. Based on the output SIR (Signal to Interference Ratio) and SDR (Signal to Distortion Ratio) analysis, the improved accuracy is derived in the proposed system. We have obtained promising results in the real experimental environment.
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使用盲源分离的消噪定制扬声器验证系统
本文提出了一种结合盲源分离噪声消除的定制说话人验证系统。该系统分为两个阶段:噪声消除阶段和说话人验证阶段。在噪声消除阶段,提出了一种基于短时傅里叶变换(STFT)的快速时频掩模技术,用于分离单个信号中两个输入声音的混合。在获得分离的语音数据后,该输入被处理到唤醒词系统。在说话人验证阶段,我们使用Mel-Frequency倒谱系数(MFCC)作为特征提取模块。然后分别使用高斯混合模型(GMM)和隐马尔可夫模型(HMM)将特征数据训练成说话人的声纹模型和状态序列模型。分析了白噪声在不同角度下对语音信号的干扰。通过对输出信号的SIR(信干扰比)和SDR(信失真比)分析,得到了系统精度的提高。我们在真实的实验环境中取得了令人满意的结果。
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