计算机科学
波形
编码器
语音识别
钥匙(锁)
灵活性(工程)
人工智能
补语(音乐)
语音活动检测
语音处理
机器学习
模式识别(心理学)
表型
数学
雷达
计算机安全
互补
化学
生物化学
操作系统
统计
基因
电信
作者
Zhongwei Teng,Quchen Fu,Jules White,Maria Powell,Douglas C. Schmidt
标识
DOI:10.1109/icpr56361.2022.9956138
摘要
An emerging trend in audio processing is capturing low-level speech representations from raw waveforms. These representations have shown promising results on a variety of tasks, such as speech recognition and speech separation. Compared to handcrafted features, learning speech features via backpropagation can potentially provide the model greater flexibility in how it represents data for different tasks. However, results from empirical studies show that, in some tasks, such as spoof speech detection, handcrafted features still currently outperform learned features. Instead of evaluating handcrafted features and raw waveforms independently, this paper proposes an Auxiliary Rawnet model to complement handcrafted features with features learned from raw waveforms for spoof speech detection. A key benefit of the approach is that it can improve accuracy at a relatively low computational cost. The proposed Auxiliary Rawnet model is tested using the ASVspoof 2019 dataset and pooled EER and min-tDCF are 1.11% and 0.03645 respectively. Results from this dataset indicate that a lightweight waveform encoder can boost the performance of handcrafted-features-based encoders for 10 types of spoof attacks, including 3 challenging attacks, in exchange for a small amount of additional computational work.
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