The trainable hearing aid:
`What will it do for clients and clinicians?
`
`By Harvey Dillon, Justin A. Zakis, Hugh McDermott, Gitte Keidser,
`Wouter Dreschler, and Elizabeth Convery
`
`INTRODUCTION: THE PROBLEM
`The extreme flexibility of modern hearing aids is present-
`ing an increasing challenge to designers and clinicians:
`How should all the adjustable, or potentially adjustable,
`amplification parameters be set to make life best for the
`intended wearer?
`Of course, all hearing aid fittings begin with a pre-
`scription of some sort. We aim to measure something about
`the client (often just the audiogram) and use this infor-
`mation to deduce how each amplification parameter (e.g.,
`gain for low-frequency, high-intensity sounds) should be
`set. Prescriptive formulas are intended to make the pre-
`scription right for the average person with a particular set
`of measured characteristics (such as an audiogram).
`Inevitably, however, some individuals will prefer set-
`tings that are different from average. Also, prescriptive for-
`mulas are derived from some combination of a theoretical
`rationale and experimental data, and possibly some for-
`mulas do not even achieve their goal of prescribing para-
`meters that are right on average, let alone for any given
`individual.1 Some parameters (e.g., speed of automatic
`noise reduction) are not individually prescribed at all. The
`designer of the hearing aid or of the fitting software sets
`them to the same value for all wearers, even though there
`may be little evidence to guide what that value should be.
`For all these reasons, it is relatively common for a hear-
`ing aid to be better matched to the individual needs of its
`wearer if adjustments are made away from the prescribed
`response. That is, most clinicians should consider the pre-
`scribed response to be a reasonable starting point, not the
`end, of the adjustment journey.
`This raises the difficult issue of how further adjustment
`should be performed. Of necessity, the clinician has made
`this adjustment, via the programming software, in the
`clinic. But there are many reasons why such an adjustment
`process may not lead to an optimal fitting:
`❖ The clinic is usually a low-noise, low-reverberation
`environment, and often the only stimulus used is the
`clinician’s voice. At best, such an adjustment can lead
`to an optimal fitting only for environments with these
`same ideal acoustic characteristics. It is well established
`that the optimal amplification characteristics vary with
`the acoustic environment.2
`❖ Other stimuli can be used, but to simulate every sit-
`uation in which people wear hearing aids would require
`a seemingly endless set of combinations. These include
`different voices at different levels, different types of
`
`noise at different levels, different degrees of reverber-
`ation, different types of non-speech sounds of inter-
`est, different directions of incoming sound (both
`sounds of interest and competing sounds), and dif-
`ferent rates of change of any of the preceding items.
`While any individual aid wearer may wish to have the
`hearing aids optimized for operation in only a hand-
`ful of places of particular importance, it would be a
`daunting task to try to approximate even these situa-
`tions in the clinic. Even if it were possible to make the
`acoustic simulation realistic, the visual environment
`would not be, which might well affect the adjustment
`that is optimal.3
`❖ If a small set of realistic environments could be cre-
`ated in the clinic, it is far from straightforward to struc-
`ture the hearing aid adjustment process so that
`adjustments made when the client is listening to one
`sound do not undo those made while the person was
`listening to previous sounds. The result may well be
`an end adjustment that is worse than the prescribed
`starting point.
`❖ Even if a converging adjustment occurs, the clinical
`time involved would be prohibitive. This will exacer-
`bate the coming problem where, due to aging of the
`population in most developed countries, there will be
`more need than ever to streamline the clinical process
`if available clinical resources are to meet the enormous
`growth in demand for services that will occur over the
`next 25 years.
`Currently, hearing aid wearers do not actually know if
`their hearing aids are optimally adjusted in any situation
`in which they use them. If the sound of the aid is so poor
`they find it unacceptable, they will return to the clinic,
`either to return the hearing aid(s) or to ask that they be
`adjusted. The clinician has to infer the acoustics of the sit-
`uation, understand the nature of the client’s dissatisfaction
`with the sound, and deduce (sometimes with software assis-
`tance) the parameter to be adjusted, the direction of the
`adjustment, and the extent of the adjustment.
`Obviously, this process may go wrong or need to be repeated
`several times, especially if the client continues to experience
`new environments with different acoustic characteristics and
`perceive that the sound is not always quite right.
`So, we are left with a problem: How can we optimally
`adjust the hearing aid? The remainder of this article describes
`a new concept, the trainable hearing aid, that can address
`this problem.
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`A trainable hearing aid
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`April 2006 • Vol. 59 • No. 4
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`A greater level of
`sophistication will
`enable the trainable
`aid to match the vol-
`ume setting the client
`selected to the acou-
`stic environment at
`the time the adjust-
`ment was made. For
`example, the hearing
`aid could measure and
`record the overall level
`of sound in the envi-
`ronment for the few
`seconds prior to the
`volume control being
`adjusted. After being
`adjusted several times
`in different environments, the hearing aid
`develops a picture of how much gain the
`client prefers in each environment.
`Figure 1 shows what this might look
`like after just six adjustments of the hear-
`ing aid. As would be expected, the wearer
`generally prefers less gain (averaged across
`frequencies) as the environment gets
`louder. Armed with this information, how
`much gain would the ideal hearing aid
`automatically select if the user were then
`to enter a new environment with an over-
`all level of, say, 80 dB SPL? The hearing
`aid could estimate this by fitting a curve
`(or in this case a two-segment straight line)
`to the data available to it. From the line
`in Figure 1, the hearing aid would deduce
`that the wearer is likely to select a gain of
`about 12 dB in this new situation, even
`though the user has never provided any
`training for this specific input level.
`If the trainable aid does its job well
`enough, the wearer will not need to touch
`the volume control in this situation.
`Although the wearer has had to do only
`a single task—varying the volume con-
`trol—the hearing aid has been able to
`deduce how the gain should vary as the
`input level changes. That is, the individ-
`
`ual gain adjustments have trained the
`device to have an individually optimized
`low-level gain (in this case 24 dB), com-
`pression ratio (1.7), compression thresh-
`old (55 dB SPL), and, therefore, absolute
`gain for any input level.
`Figure 2 shows a general block dia-
`gram for one form of a trainable hearing
`aid. For the relatively simple version of
`the trainable aid discussed above, the
`acoustic measurement module would con-
`sist of a simple sound-level meter that cal-
`culates overall sound pressure level
`averaged over a few seconds. The pro-
`grammable amplifier is no different from
`the (digital processing) amplifier in any
`current non-linear hearing aid. It’s just
`that its programming inputs remain per-
`manently (and internally) connected to
`the learning algorithm module rather than
`being disconnected once the clinician has
`finished programming the hearing aid.
`The simplest way to think about the
`learning algorithm module is that it main-
`tains a record of user adjustments and the
`corresponding acoustic environments,
`along with the appropriate statistical or
`mathematical processes to deduce the set
`of amplification parameters that best
`match the user’s preferences. In practice,
`there are ways to achieve this without
`requiring the learning algorithm to mem-
`orize every single adjustment and corre-
`sponding set of environment acoustics.
`The user control can be a simple rotary
`control, a toggle, or a pair of up-down
`buttons, and the control can be mounted
`on the hearing aid itself or on a remote
`control. The microphone and receiver are
`entirely conventional.
`Note that, based on the data in Figure
`1, the relationship between the gain pre-
`ferred and the input level is imperfect in
`that the points deviate from the fitted
`curve. For example, on the two occasions
`when the overall level in the environment
`was 60 dB SPL, what caused the wearer
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`Figure 2. Block diagram for one general form of a trainable hearing aid.
`
`Figure 1. Hypothetical data showing the gain (averaged across
`frequency) preferred by a hearing aid user in six situations
`versus the overall sound pressure level in each situation
`
`THE CONCEPT AND RANGE
`OF TRAINABILITY
`With a trainable hearing aid, the wearer
`teaches the instrument how it should be
`adjusted. The wearer does so by using the
`aid in those situations in which he or she
`would like assistance with hearing. There-
`fore, the process takes place after the client
`leaves the clinic. Once the clinician explains
`the process to the wearer, the clinician need
`not spend further time on the process.
`Trainable hearing aids will vary in their
`complexity and effectiveness. The first
`generation of device will likely be capa-
`ble of learning only one thing: the vol-
`ume control setting preferred by the client.
`A memory inside the instrument will take
`note of the setting last used by the client.
`When the hearing aid is next switched on,
`it will automatically set the volume to
`some combination of this most recent set-
`ting and the settings it remembers from
`the previous switch-on and/or from other
`times the hearing aid was used.
`If the hearing aid simply set the vol-
`ume control to the setting last used, this
`would return volume control adjustment
`to how it was before digital hearing aids.
`Gain was determined by the physical posi-
`tion of the volume control and by the
`operation of any non-linear features (e.g.,
`compression, noise reduction). Instead,
`the first generation of trainable hearing
`aids will give the user the average of the
`volume control settings the person has
`adopted previously. Thus, it will not be
`affected so much by whether the user was
`in a particularly quiet or particularly noisy
`place when he or she last adjusted the hear-
`ing aid.
`
`

`

`to select a 17-dB gain on one occasion
`but 25 dB on the other? Perhaps on the
`first occasion the dominant sound was
`rain on the roof that the person did not
`want to listen to, while on the second it
`was speech that the client particularly
`wanted to understand. Consequently,
`another level of sophistication for train-
`able hearing aids will be to measure much
`more about the environment than just the
`overall level of sound.
`For example, hearing aids could mea-
`sure the spectral shape, the rate and extent
`to which the spectral shape varies, the
`apparent direction of sound, the modu-
`lation of sound within each frequency
`channel, and the way these modulations
`match each other across channels. These
`characteristics could be individually input
`to the learning algorithm, or they could
`be combined to classify environments in
`various ways, and the resulting classifica-
`tions input to the learning algorithm.
`Obviously, a wide range of acoustic
`characteristics of sound might influence
`the type and degree of amplification the
`user would prefer. It is hard to conceptu-
`
`alize the relationships in a simple graph,
`but inferential statistics can be used to
`derive the link between these acoustic
`characteristics and the amount of gain the
`client chose. Although the hearing aid can
`never actually read the client’s mind (at
`least not in this decade), the hearing aid
`is able to “hear” everything that the client
`hears and take into account many of the
`factors that the client uses in deciding how
`far the hearing aid should be turned up
`or down.
`The trick to doing this automatically
`is that the instrument must first learn
`which factors are actually important (in
`that they seem to affect what the client
`prefers) and what the link is between the
`value of each factor and the gain preferred
`by the client. The necessary statistical oper-
`ations are well within the signal process-
`ing capabilities of the hearing aids that will
`be appearing over the next few years. Many
`of the more sophisticated acoustic para-
`meters mentioned here are already being
`measured in current hearing aids, and the
`measurements are used to control ampli-
`fication in a way determined by the hear-
`
`ing aid designer or by the clinician. By
`contrast, in the trainable aid, the client
`determines the way these acoustic para-
`meters affect the amplification provided.
`
`ADJUSTING THE GAIN-
`FREQUENCY RESPONSE
`So far we have talked only about the hear-
`ing aid automatically—but very intelli-
`gently—adjusting the gain in the same
`way as the user would adjust the volume
`control as situations changed. A hearing
`aid that almost always gave the loudness
`that the user preferred would be a great
`advance over existing hearing aids.4-6
`However, as clinicians know only too
`well, there are many other things to adjust
`in a hearing aid. For example, the amount
`of amplification has to vary with fre-
`quency, and the amount at each frequency
`has to vary with input level, usually by
`different degrees. Therefore, the next level
`of sophistication for the trainable hearing
`aid is the individual adjustment of the
`shape of the gain-frequency response.
`At first sight, it might seem necessary
`to present the user with something like
`
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`an audio mixing console, or at least a set
`of tone controls, to achieve this. How-
`ever, the trainable aid has to be simple
`enough for people to use it with minimal
`instruction.
`One solution, which we have success-
`fully used in some of our experimental
`work with trainable aids, is to have a sin-
`gle control that takes on different func-
`tions from time to time.7 For example, it
`might be a volume control when first
`adjusted, then become a treble control,
`which turns into a bass control, before
`once again becoming a volume control.
`If the user had to understand exactly
`what was occurring at any time, this would
`be a nightmare for them to use. However,
`if the instruction to the wearer is simply,
`“Turn this control to the position where
`the hearing aid sounds best, and then leave
`it there for 10 seconds or more,” the client
`actually has a simple task to perform, even
`though the amplification parameter being
`trained varies from time to time.
`A different solution is to give the user
`a small number of controls, preferably
`mounted on a remote control. The remote
`
`control could be discarded once training
`was completed.
`Our experimentation to date has indi-
`cated that users can reliably operate up to
`three controls that, between them, gov-
`ern the broad shape of the gain-frequency
`response.8 Indeed, there are several viable
`ways that the controls can be linked to
`the shape of the gain-frequency response,
`although our experimental subjects pre-
`ferred some arrangements to others.
`By analogy with the simple volume
`control trainable aid, if the user can adjust
`the gain-frequency response at any one
`input level and if this is repeated in mul-
`tiple environments, each with its own
`overall input level and spectral shape, then
`the learning algorithm is able to deduce,
`within each frequency region, the desired
`absolute gain at each input level, and thus
`the individually preferred compression
`ratios and compression thresholds.
`In principle, there is no amplification
`parameter that cannot be trained by the
`user, provided he or she can hear some-
`thing change in the sound quality as the
`underlying parameter is varied by moving
`
`the control throughout its range. Quan-
`tities such as depth and speed of automatic
`noise suppression, selection and speed of
`adaptive directionality, compression speed,
`spectral enhancement, and frequency trans-
`position can all be subject to training. In
`general, the more parameters we attempt
`to train, the more time the user will need
`to spend training the device.
`Our experimental data with a trainable
`aid that controlled the non-linear gain-fre-
`quency response suggest that, not sur-
`prisingly, the trained hearing aid became
`more strongly preferred to non-trainable
`(but otherwise identical) hearing aids the
`longer the user spent training it.7 A few
`weeks of training is certainly long enough
`to accumulate the necessary number of aid
`adjustments to achieve a significant pref-
`erence for the trained aid. This preference
`was marked and significant, even under
`double-blinded conditions.7
`Clients do typically appear to be will-
`ing to spend a few weeks training their
`hearing aids, though some are willing to
`spend only a few days and others are will-
`ing to spend months.9 The effort may not
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`

`be any greater than the client would oth-
`erwise have spent adjusting a conventional
`volume control. And, unlike a conven-
`tional hearing aid, the need to adjust
`should decrease as the hearing aid learns.
`Because trainability can be used in so
`many ways, there is unlikely to be a sin-
`gle “trainable hearing aid.” Rather, many
`companies will introduce trainability, and
`different devices will vary in their method
`of user control, the aspects of the acoustic
`environment that are monitored, and the
`amplification parameters that are con-
`trolled.
`Having explained what we mean by a
`trainable hearing aid, let’s distinguish it
`from a data-logging aid. A trainable hear-
`ing aid may well require the hearing aid
`to log data—user inputs, at least, and
`acoustic environment characteristics for
`a more sophisticated device. Furthermore,
`the data logged by a trainable hearing aid
`could subsequently be perused by the clin-
`ician (in person or over the Internet),
`which is another similarity with data-log-
`ging aids. However, if the hearing aid only
`logs data, for later perusal and action by
`the clinician or the fitting software, and
`does not automatically change amplifica-
`tion on the basis of input from the user
`that it logs, it is not user-trainable.
`
`IMPACT IN THE CLINIC
`Trainability is likely to have a profound
`effect on how hearing aids are prescribed,
`measured, and adjusted in the clinic. It
`may also affect how a succession of
`appointments is structured, and signifi-
`cantly decrease the average number and/or
`length of appointments.
`Most obviously, if the purpose of train-
`ability is to enable the client to customize
`the broad shape of the gain-frequency
`response to his or her particular listening
`environments, then there is no point in
`spending valuable clinical time achieving
`a close match to a prescribed response.
`While a good prescription should still be
`used to ensure that the sound is safe and
`reasonable from the first time the hearing
`aid is worn, it does not seem worthwhile
`spending the time to do real-ear mea-
`surements and subsequent adjustments,
`provided that the manufacturers’ software
`is reasonably accurate at approximating
`the prescription from the outset.
`Further time saving should occur
`whenever a client contacts the dispenser
`
`to complain about the sound quality. Sup-
`pose, for example, that the client finds
`that the hearing aid sounds unpleasantly
`loud and rumbly in traffic noise. Instead
`of scheduling the client for an adjustment
`appointment (or worse, advising the client
`that his or her brain will get used to it and
`accommodate!), the clinic will simply
`instruct the client to head for the nearest
`traffic and repeatedly adjust the control
`to whatever sounds best in that situation.
`Assuming the trainable aid enables the
`client to control the gain-frequency
`response, it will progressively turn down
`the low-frequency gain for high-level
`sounds, if that modification will solve the
`problem. If the trainable aid has sophis-
`ticated environment-monitoring capabil-
`ities and if it is only in traffic noise that
`the client benefits from this adjustment,
`then the automatic adjustment will take
`place only in traffic noise and not affect
`other high-intensity,
`low-frequency
`sounds, such as the wearer’s own voice.
`Pretty quickly, the client will learn that
`the permanent solution to unacceptable
`sound in any situation is to use, and for
`a limited time, adjust the hearing aid in
`that situation. In the process, some clients
`will likely begin to feel more “ownership”
`over the adjustment of the hearing aid
`than if they regard it as something beyond
`their control. Other clients will no doubt
`prefer to leave everything to “the expert.”
`A survey of the clinical (adult, hear-
`ing aid wearing) population has demon-
`strated an overwhelmingly positive
`response to the concept of trainability.9
`Our hope is that trainable hearing aids
`will lead to more effectively customized
`hearing aids, as well as to a greater sense
`of ownership of the fitting, and that this
`combination of improvements will reduce
`the incidence of unused and returned
`hearing aids. However, we will have no
`data on this until trainable hearing aids
`are available in a commercial and cos-
`metically acceptable form.
`
`OTHER POTENTIAL BENEFITS
`Trainable hearing aids could also be set to
`detect long-term changes in the gain
`selected by the user. As the most likely
`explanations for this would be deteriora-
`tion of the user’s hearing or the hearing
`aid’s becoming faulty (e.g., progressively
`blocked with wax), it would even be pos-
`sible to program the hearing aid to warn
`
`the wearer that it is time for a return visit
`to the hearing clinic. What clients think
`about their hearing aid talking to them
`(preferably at some quiet moment) has
`not yet been tested by our research team!
`Clinicians have long suspected that
`hearing aid wearers’ amplification require-
`ments change once they acquire experi-
`ence with the hearing aids. It is commonly
`believed that new users prefer less overall
`gain and less high-frequency emphasis
`than experienced users.
`Several manufacturers include acclima-
`tization or adaptation managers in their
`software to modify the standard pre-
`scriptions. While research support for these
`views is minimal,10,11 the trainable aid
`provides an easy way to allow for these
`effects if the concern is real and causes no
`disadvantage if it is not. Users can start
`and stop training the trainable aid any
`time they wish. A reasonable clinical strat-
`egy would therefore be to instruct clients
`to train the aid intensively during the first
`few weeks, then forget about training and
`enjoy the benefits of the trained aid for
`the next few months. At some later stage,
`they could resume training for an addi-
`tional week or two, and if the user’s pref-
`erences have changed, the trainable aid
`will accommodate to such changes with-
`out further involvement by the clinician.
`Clinicians may wonder if they will still
`be needed at all if the trainable hearing
`aid becomes popular. The answer is an
`easy yes, though how they spend their
`time may well change. If they spend less
`time on hearing aid measurement, adjust-
`ment, and re-adjustment, then they will
`have more time for:
`❖ Understanding clients’ needs, beliefs,
`and motivation or lack of motivation
`prior to a fitting. Extensive research
`indicates that a client’s motivation to
`use hearing aids is an important deter-
`minant of success with amplifica-
`tion.12-16 Additional counseling
`aimed at uncovering clients’ beliefs
`and providing additional perspectives
`for them to consider prior to fitting
`may be effective in increasing hear-
`ing aid use and satisfaction.
`❖ Giving information after the hearing
`aid fitting about the use of assistive lis-
`tening devices, communication or envi-
`ronmental strategies in difficult listening
`situations, and telephone usage.
`❖ Assessing and fitting additional
`
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`clients, given the likely steep rise in
`demand for services.
`❖ Managing future clients who will have
`complex devices that combine the
`functionality of cochlear implants and
`hearing aids.
`❖ Dealing with the logistical issues that
`will arise from the likely merging of
`hearing aids and other communica-
`tion/entertainment/functional devices
`that provide speech to the ear canals
`(for people with mild hearing loss).
`Time saving will occur only with those
`clients who actually receive a trainable aid.
`We know that it will not be suitable for
`all clients (see next section), so the clini-
`cian will have to determine which clients
`should be offered a trainable aid, and what
`complexity of aid to offer them.
`
`IMPACT ON USERS
`We have already mentioned numerous
`positive impacts of trainability on the hear-
`ing aid wearer. These include:
`❖ A hearing aid with amplification cus-
`tomized by the wearer to his or her
`individual preference in the actual
`environments in which the device will
`be worn.
`❖ Fewer visits to the clinic, especially
`for people initially dissatisfied with
`the sound of their hearing aids.
`❖ The ability for wearers to directly re-
`customize their hearing aids if their
`preferences change following experi-
`ence with the hearing aids, if the hear-
`ing aids are worn in new acoustic
`environments, or if the wearer’s hear-
`ing loss fluctuates.
`❖ The ability to have more acoustic
`parameters customized to preferred
`values than is possible within the time
`constraints of clinical appointments.
`❖ Greater psychological “ownership” of
`the hearing aid fitting and adjustment
`process.
`Against these advantages, the aid
`wearer has to spend time training the hear-
`ing aid(s) during the first few weeks of
`use, which may be inconvenient. Based
`on our experience with experimental sub-
`jects who have evaluated a prototype train-
`able aid and based on the views that we
`sought from more than 100 hearing aid
`wearers who were allowed to try the con-
`trols of a highly trainable device, we expect
`that at least half of hearing aid wearers
`will consider that the benefits of train-
`
`ability outweigh the disadvantages. Our
`profile analyses suggest that factors such
`as gender, age, education, and attitude
`toward technology can be used to predict
`if a client is likely to be a candidate.9
`Even wider application will be found
`if the degree of trainability selected matches
`the technical sophistication of the wearer.
`For example, anyone who can physically
`manipulate a volume control (on the hear-
`ing aid or on a remote control) could use
`a trainable aid that simply made the vol-
`ume control operate in a more intelligent
`fashion. By contrast, the use of a two-con-
`trol trainable aid in which each control
`changes its function from time to time
`would require the wearer to possess a
`higher degree of cognitive functioning.17
`There will doubtless continue to be a need
`for accurately prescribed, fully automatic
`hearing aids without controls for those
`clients least able to operate hearing aids.
`One doubt that might be raised about
`the trainable aid is that it provides the
`amplification characteristics the wearer
`prefers, not necessarily those that are best
`for the wearer. We don’t think this is a
`valid criticism. Who is to say, with any
`certainty, what is best for a client? One
`would need to be extremely confident
`about a prescription to assert that it is best
`for the client when the client is asserting
`that, all things considered, some alterna-
`tive response was better.
`If the prescribed responses really were
`best for everyone, hearing aid fitting
`would be a straightforward mechanical
`process in which fine-tuning based on
`client report was never necessary. Pre-
`scriptive procedures usually try to achieve
`just one or two aims. For example, NAL-
`NL1 seeks to maximize intelligibility
`while keeping overall loudness less than
`or equal to normal.18
`A hearing aid wearer is likely to have
`additional goals, such as maximizing nat-
`uralness, maximizing localization ability,
`or minimizing the distracting effects of
`certain sounds. In our view, the client is
`better able to make a balanced choice
`between alternative amplification charac-
`teristics than anyone else. The client can
`self-assess the ease of understanding speech
`with each amplification alternative offered,
`and weigh this in with other considera-
`tions. We hope that when clients reliably
`get amplification adjusted in the way they
`think is best, more of those who need
`
`amplification will appreciate the many
`advantages of hearing aids.
`
`Harvey Dillon, PhD, is Director of Research at National Acoustic
`Laboratories (NAL) and Deputy Director of the Cooperative Research
`Centre for Cochlear Implant and Hearing Aid Innovation (CRCCIHAI).
`Justin A. Zakis, PhD, was Research Scientist at CRCCIHAI and at
`the Bionic Ear Institute and is now at Dynamic Hearing. Hugh McDer-
`mott, PhD, is Associate Professor at CRCCIHAI and at the University
`of Melbourne. Gitte Keidser, PhD, is Senior Research Scientist at
`CRCCIHAI and at NAL. Wouter Dreschler, PhD, was visiting sci-
`entist at CRCCIHAI and is Professor of Clinical and Experimental Audi-
`ology at
`the Academic Medical Center, Amsterdam.
`Elizabeth Convery, MAud, is research audiologist at CRCCIHAI
`and at NAL. Readers may contact Dr. Dillon at Harvey.dillon@nal.gov.au.
`
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`36
`
`The Hearing Journal
`
`A trainable hearing aid
`
`April 2006 • Vol. 59 • No. 4
`
`