Hansen John H L, Lee Jaewook, Ali Hussnain, Saba Juliana N
Cochlear Implant Processing Laboratory, Center for Robust Speech Systems (CRSS-CILab), Department of Electrical Engineering and Computer Science, University of Texas at Dallas, Richardson, Texas 75080, USA.
J Acoust Soc Am. 2020 Mar;147(3):1418. doi: 10.1121/10.0000690.
The goal of this study is to determine potential intelligibility benefits from Lombard speech for cochlear implant (CI) listeners in speech-in-noise conditions. "Lombard effect" (LE) is the natural response of adjusting speech production via auditory feedback due to noise exposure within acoustic environments. To evaluate intelligibility performance of natural and artificially induced Lombard speech, a corpus was generated to create natural LE from large crowd noise (LCN) exposure at 70, 80, and 90 dB sound pressure level (SPL). Clean speech was mixed with 15 and 10 dB SNR LCN and presented to five CI users. First, speech intelligibility was analyzed as a function of increasing LE and decreasing SNR. Results indicate significant improvements (p < 0.05) with Lombard speech intelligibility in noise conditions for 80 and 90 dB SPL. Next, an offline perturbation strategy was formulated to modify/perturb neutral speech so as to mimic LE through amplification of highly intelligible segments, uniform time stretching, and spectral mismatch filtering. This process effectively introduces aspects of LE into the neutral speech, with the hypothesis that this would benefit intelligibility for CI users. Significant (p < 0.01) intelligibility improvements of 13% and 16% percentage points were observed for 15 and 10 dB SNR conditions respectively for CI users. The results indicate how LE and LE-inspired acoustic and frequency-based modifications can be leveraged within signal processing to improve intelligibility of speech for CI users.
本研究的目的是确定在噪声环境中，人工加大音量的言语对人工耳蜗（CI）使用者的言语可懂度可能带来的益处。“伦巴德效应”（LE）是指在声学环境中，由于噪声暴露，通过听觉反馈来调整言语产生的自然反应。为了评估自然产生和人工诱导的伦巴德言语的可懂度表现，我们生成了一个语料库，通过在70、80和90分贝声压级（SPL）下暴露于大量人群噪声（LCN）来创建自然伦巴德效应。纯净语音与信噪比为15和10分贝的LCN混合，并呈现给五名CI使用者。首先，将言语可懂度作为加大的伦巴德效应和降低的信噪比的函数进行分析。结果表明，在80和90分贝声压级的噪声条件下，伦巴德言语的可懂度有显著提高（p < 0.05）。接下来，制定了一种离线扰动策略，对中性语音进行修改/扰动，以便通过放大高可懂度片段、均匀时间拉伸和频谱失配滤波来模拟伦巴德效应。这一过程有效地将伦巴德效应的各个方面引入到中性语音中，其假设是这将有利于CI使用者的可懂度。在15和10分贝信噪比条件下，CI使用者分别观察到可懂度显著提高（p < 0.01），提高了13和16个百分点。结果表明了如何在信号处理中利用伦巴德效应以及基于伦巴德效应的声学和频率修改来提高CI使用者的言语可懂度。
