首页|基于双微麦克风阵列与Wide ResNet网络的语音命令词识别

基于双微麦克风阵列与Wide ResNet网络的语音命令词识别

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为了提高噪声环境下语音识别的稳健性[1],提出宽残差深度神经网络的语音识别算法。该算法结合双微麦克风阵列系统、语音数据集为双微麦克风数据集,使用功率归一化倒谱系数作为特征参数输入到残差网络中进行训练。实验表明,与ResNetl5模型、ResNet18模型相比,只有三个残差模块的宽残差网络在噪声环境下语音命令词的识别和内外部说话人检测任务中具有较高的准确度,均达到了 95%以上。
SPEECH COMMAND WORD RECOGNITION BASED ON DUAL MICRO MICROPHONE ARRAY AND WIDE RESNET
In order to improve the robustness of speech recognition in noise environment,a speech recognition algorithm based on wide residual deep neural network is proposed.The algorithm combined the dual micro microphone array system,and the voice data set was the dual micro microphone data set.The power normalized cepstrum coefficient was used as the characteristic parameter to input into the residual network for training.Experimental results show that,compared with the Resnet15 model and Resnet18 model,the wide ResNet with only three residual modules has higher accuracy in the recognition of speech command words and the internal and external speaker detection task under noise environment,both reaching more than 95%.

Speech recognitionWide ResNetPower normalized cepstrum coefficientDual micro microphone array

祁潇潇、曾庆宁、赵学军

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桂林电子科技大学信息与通信学院 广西桂林 541004

语音识别 宽残差神经网络 功率归一化倒谱系数 双微麦克风阵列

国家自然科学基金广西自然科学基金重点项目广西无线宽带通信与信号处理重点实验室项目

619610092016GXNSFDA380018GXKL06200107

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(5)
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