SoftVoice Improves Speech Recognition and Reduces Listening Effort in Cochlear Implant Users

Objectives: 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.

Design: 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.

Results: 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.

Conclusions: 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.