Lai Ying-Hui, Zheng Wei-Zhong, Tang Shih-Tsang, Fang Shih-Hau, Liao Wen-Huei, Tsao Yu
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:404-408. doi: 10.1109/EMBC.2018.8512277.
The performance of a deep-learning-based speech enhancement (SE) technology for hearing aid users, called a deep denoising autoencoder (DDAE), was investigated. The hearing-aid speech perception index (HASPI) and the hearing- aid sound quality index (HASQI), which are two well-known evaluation metrics for speech intelligibility and quality, were used to evaluate the performance of the DDAE SE approach in two typical high-frequency hearing loss (HFHL) audiograms. Our experimental results show that the DDAE SE approach yields higher intelligibility and quality scores than two classical SE approaches. These results suggest that a deep-learning-based SE method could be used to improve speech intelligibility and quality for hearing aid users in noisy environments.
研究了一种用于助听器用户的基于深度学习的语音增强（SE）技术，即深度去噪自动编码器（DDAE）。听力辅助语音感知指数（HASPI）和听力辅助声音质量指数（HASQI）是用于评估语音清晰度和质量的两个著名评估指标，用于在两种典型的高频听力损失（HFHL）听力图中评估DDAE SE方法的性能。我们的实验结果表明，DDAE SE方法比两种传统的SE方法产生更高的清晰度和质量分数。这些结果表明，基于深度学习的SE方法可用于改善嘈杂环境中助听器用户的语音清晰度和质量。
