Improving Speech Recognition in Bilateral Cochlear Implant Users by Listening With the Better Ear

Introduction

Cochlear implants (CIs) are being provided at an increasing rate to those with a severe-to-profound hearing loss in order to restore hearing. CIs restore hearing by taking an acoustic sound signal and converting it into electrical stimulation by separating the incoming sound into a number of channels with different center frequencies. Then, in each channel, the slow-varying envelope of the signal is extracted and used to modulate the amplitude of electrical pulses. These pulses stimulate different parts of the cochlea, taking advantage of its tonotopic organization. Through this mode of stimulation, the profoundly deaf have been able to recover a remarkable amount of speech understanding, especially in quiet conditions, where performance on understanding sentences can be as high as 100% in some users (Firszt et al., 2004; Loizou, Mani, & Dorman, 2003; Wilson & Dorman, 2007). Recently, bilateral implantations have become more common, with demonstrated benefits in sound localization and understanding speech-in-noisy situations when compared with single CI use (Litovsky, Parkinson, Arcaroli, & Sammeth, 2006; Litovsky et al., 2004, Litovsky, Parkinson, & Arcaroli, 2009; van Hoesel & Tyler, 2003). However, even with two implants, CI users typically do not perform as well as normal hearing (NH) listeners in the same tasks, especially in the presence of noise (Kerber & Seeber, 2012; Loizou et al., 2009; Misurelli & Litovsky, 2012). In particular, speech reception thresholds (SRTs), that is, the signal-to-noise ratio (SNR) needed to achieve 50% correct word recognition, can be 15 to 20 dB higher in CI users compared with NH listeners (Loizou et al., 2009).

Much of the current research in improving speech-in-noise understanding in bilateral CIs has focused on two areas. One area of focus has been to improve the spatial hearing abilities of bilateral CI users. This includes improving sound localization performance and speech-in-noise understanding, both of which are much better in NH listeners. A measurable spatial hearing benefit is spatial release from masking (SRM), which is the improvement in the SRT due to spatial separation of target and masker talkers. Using current sound processing strategies, bilateral CI users have shown very little benefit of SRM (2–5 dB), and much of this gain was due to monaural head shadow effects (Loizou et al., 2009). In contrast, NH listeners typically show SRM as large as 10 to 15 dB under similar conditions (Hawley, Litovsky, & Culling, 2004; Jones & Litovsky, 2011). The reason for small SRM in bilateral CI users may be due to poor sensitivity to interaural time differences (ITDs) when listening with clinical processors (Aronoff et al., 2010; Grantham, Ashmead, Ricketts, Labadie, & Haynes, 2007). It is speculated that ITDs cannot be used by CI users because they are not presented to the auditory system with fidelity with current clinical processors. This is because bilateral CIs are not synchronized and act as monaural systems when analyzing the incoming acoustic signals at each ear (Litovsky et al., 2012; van Hoesel, 2004). In addition, most processors operate at a pulse rate that is too high to provide useful ITD cues in the electrical pulses (Laback, Egger, & Majdak, 2015). This lack of access to ITDs may be limiting a bilateral CI user’s ability to take advantage of binaural unmasking mechanisms that are enjoyed by NH listeners. There are several models that describe how ITDs might be important in binaural unmasking for speech-in-noise understanding (see Colburn & Durlach, 1978 for an overview). One such model, the equalization-cancellation model (Durlach, 1972), assumes that spatial separation of the target and masker allows the auditory system to apply internal delays to compensate for the interaural configuration of the noise. Then the noise can be canceled from the binaural signal by subtraction after equalization, thereby improving the SNR. While ITD sensitivity has been poor with clinical processors, the use of synchronized research processors in the laboratory has shown that some bilateral CI users do have sensitivity to ITDs, particularly at low rates of stimulation (for a review of the literature, see Kan & Litovsky, 2015; Laback et al., 2015). This is promising for the possibility of restoring binaural unmasking benefits to bilateral CI users, but technical challenges still exist. It is likely that changes in clinical mapping practices, coordinated signal processing between processors, and new speech coding strategies will need to be developed before significant improvement in speech-in-noise understanding, and sound localization can be seen in CI users.

A second area of focus has been to improve speech-in-noise understanding by using directional or multi-microphone techniques and sometimes combined with adaptive beamforming or speech enhancement algorithms. Research in this area has focused on improving the SNR. Commercially, Advanced Bionics users have the option of using a T-Mic™ adaptor which sits in front of the ears to take advantage of the natural acoustic filtering of the pinna to attenuate sounds coming from behind a listener. Using the T-Mic adaptor, it has been shown that SRTs can be improved by 4 dB over the behind-the-ear microphone, though an average of + 10 dB SNR is needed for 50% correct speech understanding (Gifford &d Revit, 2010). In the Cochlear Nucleus Freedom processor, the use of BEAM™ (a two-microphone adaptive beamforming algorithm) has been shown to improve SRTs by about 6 dB over a single directional microphone. However, an average SNR of + 6.2 dB is needed to achieve 50% correct speech understanding (Spriet et al., 2007). The Cochlear Nucleus 5 processor has a dual-microphone preprocessing scheme (marketed as Zoom) that provided some improvement over BEAM. Using Zoom, an average of + 2 dB SNR is needed to achieve 50% correct speech understanding (Wolfe et al., 2012). More recently, multi-microphone techniques have been implemented to that take advantage of the additional microphones made available through bilateral implantation. For example, Advanced Bionics has implemented StereoZoom in their commercial processors, which is a four-microphone adaptive beamformer that takes advantage of all the microphones in the processors across the ears. This binaural beamforming algorithm has been shown to provide approximately 7.1 dB improvement compared with listening with an omnidirectional microphone (Buechner, Dyballa, Hehrmann, Fredelake, & Lenarz, 2014). In the laboratory, where there are less computational and power limitations compared with existing processors, more sophisticated algorithms have been evaluated. Many of these approaches have either applied an ideal binary mask that fully eliminate time-frequency bins that have poor SNR (e.g., Hu & Loizou, 2008; Koning, Madhu, & Wouters, 2015), or more sophisticated filtering algorithms that reduce the noise by estimating the noise power (e.g., Goldsworthy, Delhorne, Desloge, & Braida, 2014; Hersbach, Grayden, Fallon, & McDermott, 2013; Koning et al., 2015), or enhancing the target signal (e.g., Healy, Delfarah, Vasko, Carter, & Wang, 2017; Kokkinakis, Azimi, Hu, & Friedland, 2012). Baumgärtel, Hu et al. (2015) provides a thorough evaluation of some of the most promising algorithms and showed that these algorithms can provide up to 7 dB improvement in CI users in demanding noisy conditions. Despite these advances in signal processing, NH listeners can still perform far better than CI listeners at adverse SNRs.

In this work, a novel listening strategy is proposed for improving speech-in-noise understanding in CI users. The premise of this work is to show proof-of-concept that speech-in-noise listening can be better than just signal processing alone, if we try to take advantage of the inherent speech unmasking asymmetries of bilateral CI users. This work was motivated by recent results reported in Goupell, Kan, and Litovsky (2016). In one of the listening conditions in Goupell et al.’s study, a female target talker was presented to one ear while a male masker talker was presented to the contralateral ear, simultaneously. The talkers were presented through the auxiliary port of clinical processors to remove crosstalk between the two ears. In this condition, bilateral CI users were asked to selectively attend to the target talker, while ignoring the masker talker in the contralateral ear. An average SRT of −23 dB SNR was reported, which was a significant improvement to a second condition tested in the same listeners, where the target and masker were presented in both ears, simultaneously. In the later condition, an average SRT of −8 dB SNR was needed to achieve similar performance. This implies that by presenting target and maskers in separate ears, and instructing the listener to selectively attend to the ear with the target, an average improvement of 15 dB can be obtained when compared with the nonseparated condition. For many listeners, different SRTs were achieved depending on which ear was attended to, suggesting a better ear for attending to speech in the presence of a masker. The phenomenon of a better ear has also been reported in a number of other studies (e.g., Baumgärtel, Hu, et al., 2015; Litovsky et al., 2006; van Hoesel & Tyler, 2003). There are a number of reasons why CI users have a better or preferred ear for listening. This ear may be their first implanted ear, or the ear they always use the telephone on, or the ear that had better hearing prior to implantation, and so on. However, there may also be more objective reasons for a better ear, which include differences in the two ears in terms of duration and etiology of deafness, neural survival, and quality of electrode array implantation. These factors may affect the quality of the speech signal being presented to the brain. The existence of a better ear suggests that much better speech-in-noise performance may be achieved by exploiting this inherent asymmetry with additional signal processing. Hence, a better ear listening strategy is proposed that combines (a) a priori knowledge of a better ear with (b) a signal processing algorithm that separates the target talker from a noisy background and delivers the target talker to the better ear while the remaining sound scene is presented to the contralateral ear (Figure 1). One reason for delivering the remaining background sounds to the contralateral ear is to maintain situational awareness. While traditional beamforming and noise reduction algorithms aim to remove as much of the background noise as possible to improve SNR, this may not be desirable for CI users who want to be aware of the situation around them. The better ear strategy can help maintain situational awareness because the listener can switch attention between their ears to attend to either the target talker or surrounding background talkers. In this work, a signal processing algorithm based on Wiener filtering principles (Kan, 2017; Kan, Jin, & Van Schaik, 2008) was used to implement the better ear listening strategy and evaluated with bilateral CI users as a proof-of-concept. This algorithm is by no-means the state-of-the-art in noise reduction signal processing but was useful in this work because of the simplicity in its implementation for separating a target talker from a noisy background. OPEN IN VIEWER

Methods

Stimuli

The better ear strategy was evaluated using virtual auditory space (VAS) techniques (Carlile, 1996). The VAS used in this study simulates a situation where a target talker is in front of the listener, and masking talkers are toward the left and right. The head-related transfer functions (HRTFs) used for creating the VAS were measured individually on each participant in a 2.90 × 2.74 × 2.44 m single-walled sound proof booth using a blocked-ear canal technique (see Kan [2017] for a description of the HRTF recording method). HRTFs were measured for three loudspeaker positions located in front and on the left and right of the listener. The recording system transfer function was deconvolved from the measured HRTFs using a pseudoinverse technique (Epain et al., 2010). The HRTFs were then used to filter the speech stimuli for testing.

The speech stimuli used for testing was from Kidd, Best, and Mason (2008). The target and two interfering talkers were different male voices, speaking a five-word sentence of the form: name, verb, number, adjective, and noun. The target talker was filtered with the HRTFs corresponding to the front location, while the two interfering talkers were filtered with HRTFs corresponding to the left and right locations. All talkers began speaking their sentences simultaneously. The filtered speech stimuli were then combined at different SNRs to generate the VAS. An implementation of the better ear strategy was created by using the algorithm described in Kan (2017). Figure 1 illustrates how the algorithm was used to implement the better ear strategy. In this algorithm, there are two microphone signals, M₁ and M₂. At a particular time frame, t, and frequency bin, f, the signals captured by the two microphones can be modeled by:

M 1 ( t , f ) = A 1 ( t , f ) T ( t , f ) + B 1 ( t , f ) N ( t , f ) M 2 ( t , f ) = A 2 ( t , f ) T ( t , f ) + B 2 ( t , f ) N ( t , f )

(1)

where A₁ and A₂ are the direction-dependent gains applied by the microphones to the target signal T (for now, A₁ and A₂ are assumed to be known), and B₁ and B₂ are the unknown directional gains applied to all noise sources N in the scene. For brevity of notation, the time and frequency indices are omitted in the following derivation. By rearranging Equation (1), and letting B₂ = 1 and α = B₁/B₂, ¹

T ' = 1 A 1 - α A 2 M 1 - α A 1 - α A 2 M 2

(2)

That is, an estimate of the target, T′, can be found at a particular time frame, t, and frequency bin, f, if α can be estimated. The value α can be optimally estimated in the least-mean square error sense from the auto- and cross-correlations of M₁ and M₂, which can be written as:

E { M 1 2 } = A 1 2 E { T 2 } + B 1 2 E { N 2 } + 2 A 1 B 1 E { TN } E { M 2 2 } = A 2 2 E { T 2 } + B 2 2 E { N 2 } + 2 A 2 B 2 E { TN } E { M 1 M 2 } = A 1 A 2 E { T 2 } + B 1 B 2 E { N 2 } + ( A 1 B 2 + A 2 B 1 ) E { TN }

(3)

where E{X²} and E{XY} denote the auto-correlation of X and cross-correlation of X and Y, respectively. If the target signal is assumed to be uncorrelated with the noise (i.e., E{TN}=0), it can be shown that α can be estimated by substituting B₂ = 1 and α = B₁/B₂ into Equation (3) and rearranging:

α = A 2 E { M 1 2 } - A 1 E { M 1 M 2 } A 2 E { M 1 M 2 } - A 1 E { M 2 2 }

(4)

Hence, by solving Equations (4) and (2), an estimate of the target signal for a particular time-frequency bin can be obtained. In a similar fashion, the background noise can be estimated by rearranging Equation (1) to obtain:

N ' = A 2 α A 2 - A 1 M 1 - A 1 α A 2 - A 1 M 2

(5)

It is interesting to analyze how the value of α affects the estimated signals. When the target and background noise are not correlated, that is, E{TN} = 0, it can be seen that the signals are optimally estimated. When α = 0, there is no noise in the time-frequency tile of microphone M₁, and Equation (2) gives a normalized estimate of the target using microphone signal M₁, while Equation (5) returns the noise recorded by microphone M₂. Conversely, if α = 1, there is no target, and Equation (2) returns a value of 0, while Equation (5) returns a scaled estimate of the noise. For 0 < α < 1, an appropriate amount of M₂ is subtracted from M₁ to estimate the target and noise from Equations (2) and (5), respectively. However, if E{TN}≠0, then the error in estimating α can be found by substituting Equation (3) into Equation (4) without assuming E{TN} = 0, which yields:

α = B 1 E { N 2 } + A 1 E { TN } B 2 E { N 2 } + A 2 E { TN }

(6)

Substituting Equations (6) and (1) into Equation (2) yields:

T ' = T - [ E { TN } / E { N 2 } ] N

(7)

where the error in estimating the target is given by the second term in Equation (7). That is, if the target and background noise are correlated, the estimate of the target signal will be corrupted by some portion of the noise energy at that time-frequency bin which is related to the amount of correlation between target and background. This implies that the SNR at these time-frequency bins will be intermittently poorer. Depending on the time and frequencies where this occurs, the perceptual consequences on intelligibility may be situation dependent. To provide an objective measure for this algorithm, the intelligibility-weighted SNR (iSNR) (Greenberg, Peterson, & Zurek, 1993) was estimated using the method described in Baumgärtel, Krawczyk-Becker, et al. (2015). To calculate iSNR, two different signals, (T + N) and (T−N), are processed using the algorithm. Assuming that both signals are processed the same way by the algorithm, the processed target and noise signals can be estimated by:

T processed = 1 2 [ ( T + N ) processed + ( T - N ) processed ]

(8)

and

N processed = 1 2 [ ( T + N ) processed - ( T - N ) processed ]

(9)

From these signals, the iSNR can be calculated as the difference between the weighted SNR before (SNR _W,in ) and after (SNR _W,out ) processing:

iSNR = SNR W , out - SNR W , in

(10)

where

SNR W = ∑ k = 1 K w k SNR k

(11)

and w_k is the band importance weight associated with the kth band (American National Standards Institute, 1997). For the stimuli used in this experiment, the mean iSNR was 2.02 dB.

In this work, the algorithm was applied to the VAS signals to implement the better ear strategy. Time-frequency analysis was conducted using 1,024-sample sine-windowed frames with a 64-sample shift per frame. A 1,024-point fast Fourier transform was applied to each frame. To ensure the smoothness and stability of the weights applied to M₁ and M₂, the cross-correlation value, E{M₁M₂}, was averaged over four frames, which is approximately equal to the maximum time difference between the two ears at a sampling rate of 44100 Hz. Further, the denominators of equations (2) and (5) were inverted with regularization, where the maximum permissible amplification at 0, 500, 1000, 8000, 12000, 16000, and 22050 Hz was 0, 0, 12, 12, 12, 0, and 0 dB, respectively. All stimuli for each listener were made in advance of testing and stored as 32-bit two-channel wav files.

Results

Table 1 and Figure 2(a) show the SRTs obtained in each condition for each listener individually. SRTs when no strategy was applied (VAS only condition) ranged from −6.4 to 5.8 dB (median: 2.2 dB; mean: 1.8 dB). When the better ear strategy was applied, there was no ear that was consistently dominant across the group. However, all listeners showed improved SRTs when the applied signal processing sent the target talker to one of the ears and maskers to the contralateral ear. Across the group, SRTs for the better performing ear ranged from −9.3 to 4.8 dB (median: −4.1 dB; mean: −3.2 dB), and SRTs for the poorer ear ranged from −8.1 to 19.7 dB (median: −1.7 dB; mean: 0.2 dB). Using Friedman’s test, a significant difference was found between the SRTs for the different listening conditions, χ²(2, 11) =17.35, p = .002. Post hoc testing with Bonferroni correction revealed a significant improvement when listening with the better ear compared with the poorer ear and the VAS only condition.

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Table 1.

Listener Data.

ID	Speech reception thresholds (dB)				First implanted ear	Years between implants	Etiology
	No strategy	Better ear strategy—left	Better ear strategy—right	Kan (2017) algorithm only
IAZ	5.7	5.4	2.9		Left	1	Adult onset, hereditary
IBF	−6.4	−8.1	−9.3	−5.7	Right	1	Adult onset, hereditary
IBK	0.9	−4.1	−0.7	1.9	Left	6	Adult onset, noise-induced, possibly hereditary
IBO	0.6	−6.1	−8.7	−3.3	Right	3	Adult onset, Otosclerosis
IBR	4.8	9.3	4.8		Right	5	Adult onset, progressive
IBY	2.2	−4.4	−7.2	−0.1	Left	4	Adult onset, unknown
ICA	5.6	−3.3	−1.7		Right	7	Childhood onset, progressive, possibly from fever
ICB	−2.3	−6.6	−7	−0.3	Right	3	Childhood onset, hereditary
ICI	−0.5	−6.3	−6.4	−0.7	Left	1	Adult onset, unknown
ICP	5.8	4	19.7	7.8	Left	3	Childhood onset, nerve damage
ICV	3	−0.7	1.9		Simultaneous	0	Adult onset, traumatic injury

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Figure 2.

(a) The SRTs obtained in each listening configuration. (b) The improvement in SRTs when listening with the proposed strategy.

The benefit when listening with the proposed strategy can be more clearly seen in Figure 2(b). No listener showed a decrement in performance when listening with the better ear, and three listeners obtained improvements of ∼ 9 dB. Median and mean improvement with the better ear strategy was 4.7 and 4.9 dB, respectively. When listening with the poorer ear, most listeners still showed some improvement suggesting that the applied algorithm could provide some improvement in listening. Median and mean performance when listening with the poorer ear was 1.7 and 1.5 dB, respectively.

Discussion

In this work, a better ear listening strategy was proposed to take advantage of an asymmetry in speech understanding performance inherent in bilateral CI users to improve speech-in-noise understanding by applying some signal processing. Using a Wiener filtering algorithm to implement the proposed strategy, most listeners gained a significant benefit in speech recognition scores, and no listeners had a decrement in speech recognition with the proposed strategy. However, during testing, it was realized that it was necessary to determine whether listening to the better ear provided additional benefit over just using the Kan (2017) algorithm for simple noise reduction in both ears. Hence, seven listeners were tested during a return visit to the laboratory in a configuration where the Kan (2017) algorithm was used as a noise reduction algorithm in both ears. Their SRTs are shown in Table 1 and Figure 2. Mean SRT was −0.1 dB SNR which was significantly poorer than listening with the better ear, χ²(1, 7) = 7, p = .008. This suggests that much of the gain in performance is from listening to the better ear rather than the algorithm itself.

Using the current implementation of the better ear listening strategy, the average SRT was −3.2 dB SNR. Compared with existing noise-reduction methods, where positive SNRs are needed to achieve comparable performance (e.g., 50% SRTs for Zoom requires + 2 dB SNR; Wolfe et al., 2012), this is a promising result, though it should be noted that this was a closed-set task which may have artificially enhanced performance. Further, both ears were tested using the better ear strategy which inherently introduced a bias toward observing a larger effect. However, the results can be considered a best case scenario for determining the better ear, and provides a proof of concept that bilateral CI users can take advantage of a better ear listening strategy.

Taken together, the results suggest that bilateral CI users who have a noticeable asymmetry in speech understanding performance may be able to take advantage of their better ear even without the special signal processing suggested in this article. This can be achieved by simply having the target talker on the side of their better ear and directing their attention to that ear. One could speculate that further improvements could be obtained in this configuration by applying directional beamforming in the better ear only, so that the target talker’s speech is enhanced. This means that the better ear listening strategy can be employed with currently available technology and some clinician-guided advice.

The 4.4 dB improvement in this study is much smaller than the improvement predicted from the results presented in Goupell et al. (2016). In that study, presenting target and maskers in separate ears provided a 15-dB improvement in SRTs compared with the condition where target and maskers were presented to both ears, simultaneously. One could consider the Goupell et al.’s (2016) result as the best-case scenario for the better ear listening strategy. The smaller improvement found in this study can probably be attributed to differences in the speech stimuli used in this experiment (Goupell et al. used a female target and one male masker, while in this study the target and two maskers were all male which would render the task much harder), interference from the background sounds from the contralateral ear, or artifacts that may have been introduced by the signal processing algorithm. The effectiveness of the signal processing applied in this study relies on the assumption that the target and background noise are not correlated. If this assumption does not hold, then the separation of the target from the background noise will be incomplete and residual background noise would be presented to the better ear, making the task harder. It is likely that with the use of more sophisticated signal processing, such as steered binaural beamformer (Adiloğlu et al., 2015), subspace methods (see Loizou, 2017 for review), blind source separation (see Kokkinakis & Loizou, 2010 for review), or machine learning algorithms (e.g., Healy et al., 2017), will lead to much greater improvements in performance. However, one benefit of the proposed algorithm lies in its computational simplicity and ease with which it can be added to the CI signal processing chain. While the current implementation of the algorithm was applied in the Fast Fourier transform domain, the algorithm can also be applied to the extracted signal envelopes in each channel, meaning that the number of computations necessary to calculate the necessary weights scale by the number of channels of the CI sound processor. It should be noted, however, that in the current evaluation, the location of the target was assumed to be known. This was necessary so that the gains applied by the microphone to the target signal could be accounted for. In practice, precise location tracking may be difficult in dynamic environments where either the listener, or the target, could be moving. However, given the smoothness of HRTF gains across nearby locations, and the fact that microphone gains have a scaling effect in Equations (2) and (5), it is anticipated that the need for precise location tracking will not be necessary. What is unclear at the present moment is how different amounts of reverberation will affect the performance of the algorithm.

In this work, there was no a priori knowledge of the better ear. In initial pilot testing, the methodology of Goupell et al. (2016) was tried, whereby a target talker was played in one ear and a masker in the contralateral ear as a way of assessing which ear would be better. However, results using that method did not necessarily predict the ear that would provide the best results using the Kan (2017) algorithm implementation of the better ear listening strategy. It is presently unclear how to objectively predict the better ear because factors such as years of CI or acoustic hearing experience, quality of the surgery and implantation, and etiology and neural survival may all contribute to speech understanding outcomes. Table 1 shows some of these factors for the listeners in this study. For the majority of listeners who were sequentially implanted and had greater than 3 years between implants, their first implanted ear typically showed the greatest benefit when using the better ear listening strategy. However, there are two listeners (IBY and ICA) that do not follow this trend. Further work is needed to understand how to predict the better ear.

The better ear listening strategy offers unique advantages for improving speech-in-noise understanding over existing approaches. First, it takes advantage of an asymmetry in speech unmasking performance in order to increase intelligibility of a target talker. In bilateral CI patients, this is particularly useful because an inherent asymmetry exists due to the variable nature of candidacy and implantation (Goupell et al., 2016). A second unique advantage of the better ear strategy is that it allows the CI user to maintain situational awareness, because nontarget sounds in the environment are still transmitted. This is contrary to existing approaches which aims to increase the SNR by removing as much of the background noise as possible. However, there are situations where removal of background noise may not be desirable. For example, a child in a noisy classroom needs to be able to hear input from a target (teacher), as well as supplementary input from classmates. When using a traditional beamforming strategy, the speech from the teacher is transmitted clearly, but surrounding sounds from classmates are usually suppressed. However, by using the better ear strategy, a child listening with bilateral CIs can still be aware of other students by attending to the nontarget ear. An extra option would include the ability to choose different mixing ratios for the two ears to enhance audibility of either the target or the background noise, if so desired. A third benefit of this strategy is economy; the better ear strategy can be made a part of the sound processors firmware without needing extra equipment, and hence it is cheaper to implement than existing radio frequency solutions such as FM and loop systems. This is cost efficient and makes the solution more easily accessible to CI users. Furthermore, reducing the number of physical components will simplify setup and likely reduce the load on teachers and other professionals who regularly need to learn about a student’s various assistive devices. Lastly, the better ear strategy is not CI specific, but rather, is a general strategy that can be implemented in listeners with any form of hearing impairment and present with an asymmetry in speech unmasking abilities between the ears. These may include patients who use bilateral hearing aids or are bimodal.

As with all approaches to improving SNR, there are limitations with the better ear listening strategy. In this work, it was assumed that the target signal within the sound scene was known. This is an inherent problem with all noise reduction algorithms, and few solutions exist. In current practice, the assumption is that the target talker of interest is in front of the listener and beamforming algorithms in existing CIs and hearing aids are designed to maximize sounds coming from in front of the listener. The same assumption was also made in this experiment. For the better ear strategy to be useful in a multitarget situation, a method for selecting and extracting multiple targets from a sound scene is required. Proposed methods for obtaining this information include pointing, target selection through button press, and visual guidance (Hart et al., 2009; Kidd, Favrot, Desloge, Streeter, & Mason, 2013).

Another limitation of this approach is that spatial hearing abilities are lost, which is a significant problem of the proposed strategy. This problem is compounded by the fact that a target talker on the right of the listener would be presented in the left ear if the left ear was the better ear. However, there may still be a group of bilateral CI users who will benefit from the better ear strategy. For some bilateral CI users, it is likely that good spatial hearing may not be possible because of limited sensitivity to binaural cues, such as ITDs, especially those who have had little acoustic hearing experience (Ehlers, Goupell, Zheng, Godar, & Litovsky, 2017; Litovsky, Jones, Agrawal, & van Hoesel, 2010). Another group who may benefit from the better ear strategy may be those who have a large asymmetry in performance between the two ears. It is possible that the proposed strategy will give them much larger benefit for understanding speech-in-noise compared with that provided via binaural hearing benefits. While the proposed strategy may not be useful as an everyday strategy because of the loss of spatial hearing abilities, the better ear strategy will likely be a useful option that a bilateral CI listener can switch to when having a conversation in demanding noisy situations.

Conclusions

Difficulty of understanding speech in noise is arguably the most common complaint of people with hearing loss. The better ear listening strategy has the potential to significantly improve speech understanding in noisy situations not just for bilateral CI users but for people with other hearing impairments as well.