通过声腔长度变形特征提升语音关键词识别准确率
Vocal Tract Length Warped Features for Spoken Keyword Spotting
- 用不同声腔长度变形特征训练单一深度网络，增强泛化能力
- 在Google命令数据集上，关键词识别准确率显著提升
- 适合需要鲁棒语音识别的嵌入式设备应用
本文提出多种融合声腔长度（VTL）变形特征的语音关键词识别方法。第一种为不依赖VTL的KWS：训练单一深度神经网络（DNN），每轮随机选取不同变形因子的VTL特征，测试时对同一语音的不同变形特征评分并等权融合。第二种方法将未变形的传统特征输入DNN进行评分。第三种为VTL拼接KWS：将多个变形后的特征拼接成高维特征用于识别。在英文Google命令数据集上的评估表明，所提方法有效提升了关键词识别准确率。
原文摘要 · Abstract (English)
In this paper, we propose several methods that incorporate vocal tract length (VTL) warped features for spoken keyword spotting (KWS). The first method, VTL-independent KWS, involves training a single deep neural network (DNN) that utilizes VTL features with various warping factors. During training, a specific VTL feature is randomly selected per epoch, allowing the exploration of VTL variations. During testing, the VTL features with different warping factors of a test utterance are scored against the DNN and combined with equal weight. In the second method scores the conventional features of a test utterance (without VTL warping) against the DNN. The third method, VTL-concatenation KWS, concatenates VTL warped features to form high-dimensional features for KWS. Evaluations carried out on the English Google Command dataset demonstrate that the proposed methods improve the accuracy of KWS.
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