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Speech Perception Enhancement Using Novel Signal Processing in Bimodal Hearing

在双模态听力中使用新型信号处理增强语音感知

基本信息

批准号：
10291578
负责人：
Yang-Soo Yoon
金额：
$ 43.19万
依托单位：
BAYLOR UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

项目摘要

Project Summary/Abstract Speech perception for those who use cochlear implants (CIs) in combination with hearing aids (HAs) in opposite ears (i.e., bimodal hearing) varies greatly. This variability depends on the users’ ability to process frequency and time information critical for speech perception. By identifying and enhancing this acoustic information, speech perception will significantly improve. In this AREA project, we aim to establish and verify a tailored identification scheme for the spectral and temporal cues responsible for consonant recognition. Our recent bimodal study shows that some frequency ranges and time segments of consonants are critical for consonant enhancement (called “target frequency or time ranges”) while other frequency and time ranges cause consonant confusions (called “conflicting frequency or time ranges”). Our Articulation Index-Gram (AI-Gram) signal processing can add and suppress intensity on these target and conflicting ranges. In Aim 1, we will determine the effect of the dead regions on consonant recognition. Target and conflicting ranges will then be identified on an individual subject basis for each consonant in the HA alone, CI alone, and CI+HA in quiet. The target frequency range will be determined by finding the frequency regions creating dramatic consonant enhancement, while the conflicting frequency ranges will be determined by finding the frequency regions creating consonant confusion. The target time ranges will be determined by finding the segment of the consonants responsible for dramatic consonant improvement while systematically truncating the consonant. The target time range will be used as the conflicting time ranges because the conflicting frequency ranges would be the most detrimental factor affecting the target frequency ranges if they coincide in time. In Aim 2, consonant recognition will be measured in quiet and noise under the three AI-Gram processing conditions: 1) target ranges alone with +6 dB gain; 2) conflicting ranges alone with -6 dB suppression; and 3) both intensified target and suppressed conflicting ranges. For each AI- Gram processing condition, consonant recognition will be measured in the matched listening conditions (e.g., the target or conflicting ranges identified in the HA alone will be presented in the HA alone listening condition). To determine how the unilateral detection ability affects bimodal benefit, the consonants processed on the target or conflicting ranges identified in the HA alone and CI alone will each be presented to the CI+HA listening condition. This proposed work will identify acoustic cues that contribute to bimodal benefit and will reveal how these cues are integrated or interfered with across modalities. Defining the relative impact of the target and conflicting ranges on the AI-Gram-sensitive consonants in the HA alone, the CI alone, and the CI+HA together will help determine the upper and lower cutoff frequencies of a HA and a CI and fine-tune these cutoff frequencies. This data is much needed for the long-term goal: developing a tailored bimodal fitting procedure. The AREA project will provide clinical research opportunities for four undergraduate students per year at Baylor.

项目总结/摘要人工耳蜗植入体（CI）与助听器（HA）联合使用者的言语感知，耳朵（即，双峰听力）变化很大。这种可变性取决于用户处理频率的能力，时间信息对语音感知至关重要。通过识别和增强这种声学信息，语音感知将得到显著改善。在这个区域项目中，我们的目标是建立和验证一个定制的识别负责辅音识别的频谱和时间线索的方案。我们最近的双峰研究表明辅音的某些频率范围和时间段对辅音增强至关重要（称为“目标频率或时间范围”），而其他频率和时间范围会导致辅音混淆（称为“冲突频率或时间范围”）。我们的清晰度指数图（AI-Gram）信号处理可以添加并抑制这些目标和冲突范围上的强度。在目标1中，我们将确定死者的影响辅音识别的区域。然后将确定个体受试者的目标和冲突范围单独HA、单独CI和安静CI+HA中每个辅音的基础。目标频率范围为通过找到产生戏剧性辅音增强的频率区域来确定，而冲突的频率范围将通过找到产生辅音混淆的频率区域来确定。目标时间范围将通过找到负责戏剧性辅音的辅音段来确定改进，同时系统地截断辅音。目标时间范围将用作冲突的因为冲突的频率范围将是影响目标的最有害因素频率范围，如果它们在时间上一致。在目标2中，辅音识别将在安静和噪音中进行测量在三种AI-Gram处理条件下：1）单独的目标范围，增益为+6 dB; 2）冲突范围单独与-6 dB抑制;和3）增强的目标和抑制的冲突范围。对于每个AI- 克处理条件，辅音识别将在匹配的听音条件下进行测量（例如，单独在HA中识别的目标或冲突范围将在单独HA监听条件中呈现）。为了确定单侧检测能力如何影响双峰效益，或者单独在HA和单独在CI中识别的冲突范围将分别呈现给CI+HA监听条件这项拟议的工作将确定有助于双峰效益的声学线索，并将揭示如何这些提示在模态之间被集成或干扰。确定目标的相对影响，单独HA、单独CI和CI+HA中AI-革兰氏敏感辅音的冲突范围将有助于确定HA和CI的上限和下限截止频率，并微调这些截止频率频率.这些数据对于长期目标是非常需要的：开发定制的双峰拟合程序。该地区项目将提供临床研究的机会，每年在贝勒大学的四个本科生。