| 周峻林,胡晓光,黄子旭,汪 旭,付哲宇.合成语声的声学分析及识别特征算法*[J].,2024,43(1):131-141 |
| 合成语声的声学分析及识别特征算法* |
| Acoustic analysis and recognition feature algorithm of synthetic speech |
| 投稿时间：2022-09-20 修订日期：2024-01-06 |
| 中文摘要: |
| 目的 当前社会新型犯罪中电信诈骗案件频发，急需一种能够自动有效区分语音真伪的方法。为进一步增强目前深度学习领域识别合成语音的能力，为保障语音信息安全提供技术上的支持。方法 本文针对合成语音声学特性上异于真实语音的特点，分析对比合成语音和真实语音的声学特性，设计了一种声学特征RMSA量化语音音强变化程度，结合FFV和SNS声学特征进行融合，量化了声学特性差异，聚焦了合成语音中关键声学信息。结果 在神经网络模型中融合输入声学特征，在FoR数据集的验证集上得到了0.6%的等错误率，在测试集上最好结果达到了10.8%的等错误率。结论 成功实现了对合成语音的识别，证实了声学特征的有效性和本文研究方案的可行性，在一定程度上拓宽了合成语音特征设计的研究思路。 |
| 英文摘要: |
| Objective With the frequent occurrence of telecommunication fraud cases in the current new social crimes, a method that can automatically and effectively distinguish the authenticity of speech is urgently needed. To further enhance the current capability of detecting synthetic speech in the field of deep learning and to provide technical support for securing speech information. Methods we analyze and compare the acoustic characteristics of synthetic speech and real speech, design an acoustic feature RMSA to quantify the variation of speech intensity, combine FFV and SNS acoustic features for fusion, quantify the difference of acoustic characteristics, and focus on the key acoustic information in synthetic speech. Results The fusion of input acoustic features in the neural network model yielded an equal error rate of 0.6% on the validation set of the FoR dataset, and the best result reached an equal error rate of 10.8% on the test set. Conclusion The recognition of synthetic speech was successfully achieved, confirming the effectiveness of acoustic features and the feasibility of the research scheme of this paper, broadening the research ideas of synthetic speech feature design to a certain extent. |
| DOI：10.11684/j.issn.1000-310X.2024.01.016 |
| 中文关键词: 声学特征 音强 基频 语音频谱图 神经网络 |
| 英文关键词: acoustic features sound intensity fundamental frequency speech spectrogram neural network |
| 基金项目:中国人民公安大学新型犯罪研究专项(2021XXFZ010)；中国人民公安大学2021年度拔尖创新人才培养项目(2021yjsky017)；上海市现场物证重点实验室开放课题基金(2020XCWZK05) |
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