Whispering is a distinct form of speech known for its soft, breathy, and hushed characteristics, often used for private communication. The acoustic characteristics of whispered speech differ substantially from normally phonated speech and the scarcity of adequate training data leads to low automatic speech recognition (ASR) performance. To address the data scarcity issue, we use a signal processing-based technique that transforms the spectral characteristics of normal speech to those of pseudo-whispered speech. We augment an End-to-End ASR with pseudo-whispered speech and achieve an 18.2% relative reduction in word error rate for whispered speech compared to the baseline. Results for the individual speaker groups in the wTIMIT database show the best results for US English. Further investigation showed that the lack of glottal information in whispered speech has the largest impact on whispered speech ASR performance.

翻译：耳语是一种独特的语音形式，以其轻柔、带气息声和低沉的特性著称，常用于私密交流。耳语语音的声学特征与正常发声语音存在显著差异，且训练数据不足导致自动语音识别（ASR）性能低下。为解决数据匮乏问题，我们采用基于信号处理的技术，将正常语音的频谱特征转换为伪耳语语音的频谱特征。通过用伪耳语语音增强端到端ASR系统，我们使耳语语音的词错误率相对基线降低了18.2%。在wTIMIT数据库中，不同说话者群体的结果表明，美国英语的效果最佳。进一步研究发现，耳语语音中声门信息的缺失对耳语语音ASR性能影响最大。