量子AI在语音情感识别和语音识别中的应用
Michael Norval1, Zenghui Wang1
1Department of Electrical and Smart Systems Engineering, University of South Africa, Florida, Johannesburg 1709, South Africa.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
这项研究使用非洲语语音数据对语音情感识别 (SER) 的混合量子-经典模型进行了基准测试. 量子方法的准确性低于经典基线,突出了当前量子计算的局限性.
科学领域:
- 量子计算是一种量子计算.
- 语音 情感 识别 语言
- 机器学习 机器学习
背景情况:
- 语音情感识别 (SER) 对人机交互至关重要.
- 混合量子-经典方法为复杂的模式识别任务提供了潜力.
- 在现实数据上评估量子算法,比如非洲语语音,对于评估它们的实际可行性至关重要.
研究的目的:
- 在非洲语语库上评估SER的混合量子-经典管道.
- 将三个量子分类器 (VQC,QSVM,QAOA) 的性能与CNN-LSTM基线进行比较.
- 为混合SER系统建立一个端到端,噪声意识的基准.
主要方法:
- 使用的Mel频率 cepstral 系数 (MFCCs) 与光谱特征的调度和能量变量.
- 实现了使用角度嵌入和特征到量子位映射的经典到量子数据编码.
- 将变量量子分类器 (VQC),量子支向量机 (QSVM) 和基于QAOA的分类器与CNN-LSTM基线进行比较.
主要成果:
- 量子模型在理想的模拟中实现了41-43%的测试准确度.
- 在现实的1%NISQ噪声下,量子模型的准确性降低到34-40%.
- 在CNN-LSTM基线实现73.9%的测试准确度,显著超过量子方法.
结论:
- 与经典基线相比,当前的NISQ时代量子模型对SER的经验准确性较低.
- 该研究为混合SER系统提供了有价值的噪音意识基准.
- 对于未来的容错量子计算制度,量子子程序的非对称优势已被注意到.
关键词:
非洲语 非洲语在MFCC中,MFCC是最重要的.QAOOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA QAOA在QSVM中使用QSVM.降低噪音 减轻噪音量子机器学习就是量子机器学习.语音 情感 识别 语音 情感 识别变量量子分类器是变量的量子分类器.更多相关视频
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