Saoji Aniket A, Sheikh Bilal A, Bertsch Natasha J, Goulson Kayla R, Graham Madison K, McDonald Elizabeth A, Bross Abigail E, Vaisberg Jonathan M, Kühnel Volker, Voss Solveig C, Qian Jinyu, Hogan Cynthia H, DeJong Melissa D
Division of Audiology, Department of Otolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN 55902, USA.
Sonova Canada Inc., Kitchener, ON N2E 1Y6, Canada.
Audiol Res. 2024 Dec 13;14(6):1114-1125. doi: 10.3390/audiolres14060092.
BACKGROUND/OBJECTIVES: Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation.
Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker. Sentence recognition scores were measured for 10 hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility.
The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. Thus, DNN-based noise reduction can improve speech perception in noisy environments, potentially reducing the need for cochlear implants in some cases. The phase inversion method showed a 4-5 dB SNR improvement for the DNN-based noise reduction program compared to the other two programs. HASPI v2 predicted slightly better speech intelligibility than was measured in this study.
DNN-based noise reduction might make it difficult for some patients with significant residual hearing to qualify for cochlear implantation, potentially delaying its adoption or eliminating the need for it entirely.
背景/目的：符合人工耳蜗植入条件的成年听力受损患者，在最佳助听器条件下，无论是在安静环境还是嘈杂环境中，通过单个扬声器呈现语音和噪声时，句子识别率通常低于60%。本研究考察基于深度神经网络（DNN）的降噪对人工耳蜗评估的影响。
使用AzBio句子，通过一个扬声器，在安静和嘈杂条件（多说话者嘈杂声）下，以5分贝和10分贝的信噪比（SNR）评估言语感知。使用三种助听器程序对10名听力受损患者的句子识别分数进行测量：安静环境、噪声中的语音以及强噪声中的球形语音（基于DNN的降噪）。将言语感知结果与包括用于预测SNR改善的相位反转技术和用于预测言语可懂度的助听器言语感知指数（HASPI v2）的实验台分析进行比较。
与安静环境程序相比，强噪声中的球形语音程序使言语感知提高了20至32个百分点。因此，基于DNN的降噪可以改善嘈杂环境中的言语感知，在某些情况下可能减少对人工耳蜗的需求。与其他两个程序相比，相位反转方法显示基于DNN的降噪程序的SNR提高了4至5分贝。HASPI v2预测的言语可懂度略高于本研究中的测量结果。
基于DNN的降噪可能会使一些有显著残余听力的患者难以符合人工耳蜗植入条件，可能会延迟其应用或完全消除对它的需求。
