Stronks H Christiaan, Apperloo Eline, Koning Raphael, Briaire Jeroen J, Frijns Johan H M
Department of Otorhinolaryngology, Leiden University Medical Center, the Netherlands.
Advanced Bionics GmbH, European Research Center, Hannover, Germany.
Ear Hear. 2021 Mar/Apr;42(2):381-392. doi: 10.1097/AUD.0000000000000928.
The ability to perceive soft speech by cochlear implant (CI) users is restricted in part by the inherent system noise produced by the speech processor, and in particular by the microphone(s). The algorithm "SoftVoice" (SV) was developed by Advanced Bionics to enhance the perception of soft speech by reducing the system noise in speech processors. The aim of this study was to examine the effects of SV on speech recognition and listening effort.
Seventeen adult Advanced Bionics CI recipients were recruited and tested in two sessions. The effect of SV on speech recognition was tested by determining the SRT in quiet using the Matrix test. Based on the individual subjects' SRTs, we investigated speech-recognition scores at fixed speech levels, namely SRT -5 dB, SRT +0 dB, SRT +5 dB, and SRT +10 dB, again in quiet and using the Matrix test. Listening effort was measured at each of these speech levels subjectively by using a rating scale, and objectively by determining pupil dilation with pupillometry. To verify whether SoftVoice had any negative effects on speech perception in noise, we determined the SRT in steady state, speech-weighted noise of 60 dBA.
Our results revealed a significant improvement of 2.0 dB on the SRT in quiet with SoftVoice. The average SRT in quiet without SoftVoice was 38 dBA. SoftVoice did not affect the SRT in steady state, speech-weighted noise of 60 dB. At an average speech level of 33 dBA (SRT -5 dB) and 38 dBA (SRT +0 dB) in quiet, significant improvements of 17% and 9% on speech-recognition scores were found with SoftVoice, respectively. At higher speech levels, SoftVoice did not significantly affect speech recognition. Pupillometry did not show significant effects of SoftVoice at any speech level. However, subjective ratings of listening effort indicated a decrease of listening effort with SoftVoice at a speech level of 33 dBA.
We conclude that SoftVoice substantially improves recognition of soft speech and lowers subjective listening effort at low speech levels in quiet. However, no significant effect of SoftVoice was found on pupil dilation. As SRTs in noise were not statistically significantly affected by SoftVoice, we conclude that SoftVoice can be used in noisy listening conditions with little negative impact on speech recognition, if any. The increased power demands of the algorithm are considered to be negligible. It is expected that SoftVoice will reduce power consumption at low ambient sound levels. These results support the use of SoftVoice as a standard feature of Advanced Bionics CI fittings for everyday use.
人工耳蜗（CI）使用者感知轻声言语的能力部分受到言语处理器产生的固有系统噪声的限制，尤其是受麦克风的限制。先进仿生公司开发了“轻声语音”（SV）算法，以通过降低言语处理器中的系统噪声来增强对轻声言语的感知。本研究的目的是检验SV对言语识别和聆听努力的影响。
招募了17名成年先进仿生CI使用者，并分两次进行测试。通过使用矩阵测试确定安静环境下的言语识别阈（SRT）来测试SV对言语识别的影响。根据个体受试者的SRT，我们再次在安静环境下使用矩阵测试，研究了在固定言语水平下的言语识别分数，即SRT -5 dB、SRT +0 dB、SRT +5 dB和SRT +10 dB。通过使用评分量表主观测量每个言语水平下的聆听努力，并通过瞳孔测量法客观测量瞳孔扩张来评估。为了验证轻声语音是否对噪声环境下的言语感知有任何负面影响，我们确定了在60 dBA稳态言语加权噪声中的SRT。
我们的结果显示，使用轻声语音时，安静环境下的SRT显著提高了2.0 dB。不使用轻声语音时，安静环境下的平均SRT为38 dBA。轻声语音对60 dB稳态言语加权噪声中的SRT没有影响。在安静环境下，平均言语水平为33 dBA（SRT -5 dB）和38 dBA（SRT +0 dB）时，使用轻声语音时言语识别分数分别显著提高了17%和9%。在较高言语水平时，轻声语音对言语识别没有显著影响。瞳孔测量法在任何言语水平下均未显示轻声语音有显著影响。然而，聆听努力的主观评分表明，在33 dBA的言语水平下，使用轻声语音时聆听努力有所降低。
我们得出结论，轻声语音在安静环境下的低言语水平时能显著提高轻声言语的识别能力并降低主观聆听努力。然而，未发现轻声语音对瞳孔扩张有显著影响。由于轻声语音对噪声环境下的SRT没有统计学上的显著影响，我们得出结论，轻声语音可用于噪声聆听环境，对言语识别几乎没有负面影响（如果有影响的话）。该算法增加的功率需求可忽略不计。预计轻声语音将在低环境声级下降低功耗。这些结果支持将轻声语音作为先进仿生CI日常使用配件的标准功能。
