Ageing Int DOI 10.1007/s12126-016-9248-4
Perception of Spectral Ripples and Speech Perception in Noise by Older Adults Pitchai Muthu Arivudai Nambi 1 & Ankmnal Veeranna Sangamanatha 2 & Mysore Dwarakanath Vikas 3 & Jayashree. S. Bhat 1 & Kumara Shama 4
# Springer Science+Business Media New York 2016
Abstract Present study aimed to obtain spectral ripple discrimination threshold in older listeners and to correlate it with their speech perception abilities. In experiment I, fifteen older adults and fifteen young adults with normal hearing sensitivity were tested for spectral ripple discrimination ability in quiet (SRDT) and speech recognition in noise (SNR50). In experiment II, twelve older adults with normal hearing sensitivity were tested for spectral ripple discrimination ability in noise (SNR-SRDT) and SNR50. SRDT and SNR50 of older adults were significantly poorer than young adults. There was a significant negative correlation between SRDT and SNR50 and significant positive correlation between SNR-SRDT and SNR50. Linear regression analysis revealed that SRDT accounted for 21 % of variance in SNR50. Quadratic regression analysis revealed that SNR-SRDT accounted for 63 % of the variance in SNR50. Despite having normal hearing sensitivity, older adults exhibited reduced spectral resolution and poor speech recognition in noise. Poor speech recognition in noise in older adults could be partly due to their spectral resolution abilities. Keywords Older adults . Spectral ripple resolution . Speech in noise . Frequency resolution
* Pitchai Muthu Arivudai Nambi
[email protected];
[email protected]
1
Department of Audiology and Speech Language Pathology, Kasturba Medical College (Manipal University), Mangalore, Karnataka, India
2
University of Western Ontario, London, Canada
3
Department of Audiology, All India Institute of speech and hearing, Mysore, Karnataka, India
4
Department of Electronics & Communication, Manipal Institute of Technology (Manipal University), Manipal, Karnataka, India
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Introduction The primary complaint of aging adults is that they cannot understand speech when someone speaks with a faster rate of speech and/or in the presence of background noise. This difficulty in speech understanding can be attributed to age related changes in the auditory processing (Bergman 1980). Aging is associated with presbyacusis, in which pure tone thresholds are elevated at high frequency (Willott 1991). Such changes lead to some of the speech perception difficulties experienced by the older adults (Corso et al. 1976). However, research has shown that older adults tend to have more difficulty in understanding speech compared to younger adults even when they are matched to pure tone acuity and for the ability to understand speech in quiet (Divenyi and Simon 1999; Dubno et al. 1984; Gordon-Salant and Fitzgibbons 1995; Pichora-Fuller and Souza 2003). The possible reason could be reduced ability to process rapid changes in temporal (time) and spectral (frequency) information, which plays a major role in differentiating speech sounds (Sandra GordonSalant and Fitzgibbons 1993; Pichora-Fuller and Souza 2003; Pichora-Fuller et al. 2006). Auditory temporal processing includes coding of periodicity (phonetic cues), rhythmic pattern (syllabic cues), gap and duration (phonemic cues) (Greenberg 1996). Accessing these temporal cues is crucial for understanding speech in adverse listening condition. A number of studies have shown reduced temporal processing abilities in aging individuals (Fitzgibbons and Gordon-Salant 1994; Sandra Gordon-Salant and Fitzgibbons 1993; Kumar and Sangamanatha 2011; Lister et al. 2002; Pichora-Fuller and Souza 2003; Pichora-Fuller et al. 2006; Snell 1997; Strouse et al. 1998) and this temporal processing deficit is pronounced even when the peripheral hearing loss effect is taken into account (Kathryn Hopkins and Moore 2011). Temporal processing in older adults was poorer than younger adults even when their hearing thresholds were within normal limits (Fitzgibbons and Gordon-Salant 1994; Strouse et al. 1998). Temporal information contributes to speech intelligibility in the form of temporal envelope and temporal fine structure (TFS) (Hopkins and Moore 2010a, b; Shannon et al. 1995; Smith et al. 2002; Zeng et al. 2005). Many studies have used TFS-LF test (Hopkins and Moore 2010a, b) to compare the sensitivity of young and older adults to TFS (Hopkins and Moore 2011; Moore et al. 2012). These studies reported that, older adults have poor TFS sensitivity than young adults. Results of these studies indicated that, age has strong correlation with TFS sensitivity but not the absolute hearing sensitivity and also that, the age effect on TFS sensitivity is apparent by middle age itself. TFS is coded through synchronous coding mechanism and electrophysiological evidences indicated that, neural synchrony is diminished in older adults (Anderson et al. 2012; Clinard and Tremblay 2013; Clinard et al. 2010). TFS is a fast oscillation which requires high degree of neural synchrony. However, temporal envelope is slow fluctuation in amplitude, but the aging process affects the temporal envelope perception too. He et al. (2008) investigated the temporal envelope perception ability of older adults by measuring the modulation detection threshold (MDT). MDTs were better in younger adults when compared to older adults. Similarly, Kumar and Sangamanatha (2011) also reported decline in MDT with advancing age. Some electrophysiological evidences also indicated that temporal envelope processing is affected in older listeners. Envelope following response for amplitude modulated tones was poorer in older adults than younger adults and this age related decline in modulation encoding was more pronounced in higher modulation rates (Grose et al. 2009; Leigh-Paffenroth and Fowler 2006). Purcell et al. (2004) recorded envelope following responses for amplitude modulated
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sweeps in younger and older adults to estimate highest modulation rate at which response could be recorded. Envelope following responses could be recorded up to 494 Hz in younger adults but in older adults, responses could be recorded only up to 294 Hz. To conclude, the process of aging affects the coding of temporal properties which are essential for speech recognition in quiet and in noise. On the other hand, spectral information is required for accurate identification of consonants (Rosen 1992; Thibodeau and Van Tasell 1987) and vowels (Turner and Henn 1989). It also helps in differentiating between two speech sounds by making use of formant transitions. Spectral information is also known to provide supra-segmental information, such as prosody, which provides information related to the emotional state of the speaker. Poor spectral resolving ability could lead to impaired speech perception in adverse listening conditions. Many studies have consistently reported reduced spectral resolving ability in older adults (He et al. 1998; Phillips et al. 2000). Spectral processing is generally studied using perceptual tasks such as frequency tuning curves (Matschke 1990), notched noise method (Patterson et al. 1982), and frequency discrimination (Abel et al. 1990; He et al. 1998). The above mentioned behavioural methods are time consuming and evoke activity from spatially restricted regions of the cochlea. For discrimination of complex signal like speech, spectral information across the auditory filters has to be integrated. However, integration of this information is not straightforward as the signal processing in the auditory system is non-linear. Therefore, it is difficult to predict discrimination of complex spectral pattern from the narrow band spectral resolution measure (Supin et al. 2005). One method that can overcome all these disadvantages is spectral ripple discrimination threshold (SRDT) method which examines the ability to discriminate complex spectral pattern. Stimuli with spectral ripples consist of peaks and dips in spectral domain. Ripple density can be increased by increasing the number of ripples per octave. Spectral resolution is assessed using Bphase reversal^ (Supin et al. 1994) test, where, individual is presented with two stimuli, one with standard spectrum and the other with the reverse spectrum. Reverse spectrum had peaks, whereas the standard spectrum had dips and vice versa. The spectral ripple discrimination threshold (SRDT) is estimated by measuring highest ripple density at which the individual can differentiate the stimuli with the standard and the reverse spectrum. Studies have shown significant correlation between SRDT and speech perception in noise in individuals with normal hearing, hearing impaired and cochlear implant users (Henry et al. 2005). There is dearth of information in literature on spectral ripple perception by older adults. Hence, the current study was aimed to measure the spectral processing abilities using spectral ripple discrimination method and to study the speech perception abilities of older adults in noise. This study also aimed to understand the possible association between SRDT and speech recognition in noise.
Materials and Method Participants In experiment I, thirty participants were tested for spectral ripple discrimination threshold in quiet and the speech recognition scores in noise. Participants were divided
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into two groups. Group I consisted of 15 older adults (Mean age of 53.04 years) with the age range of 51 to 61 years, and group II consisted of 15 young adults (Mean age of 22 years) within the age range of 18 to 25 years. In experiment II, 12 older adults within the age range of 51–60 (Mean age of 55 years) participated. Individuals in this group were tested for spectral ripple discrimination ability in noise and speech recognition threshold in noise. Maintenance and Welfare of Parents and Senior Citizens Act (Ministry of Social Justice and Empowerment 2007) defines an Bolder person^ as individual who has attained the age of 60 years or above. As per this act, the term Bolder adult^ may be reserved for individuals with the age of 60 years and above in India. However, age related auditory sensory deficit does not start only at the age of 60 years. Age related changes in auditory processing, also called as Bauditory aging^ starts much before 60 years (Kumar and Sangamanatha 2011). Hence, in the current study, participants of experiment II and participants of group I in experiment I were designated as older adults even though, many participants cannot be referred as older adults as per the formal definition. So, the term Bolder adults^ may refer to this particular age group only in this article. All the participants had hearing sensitivity less than 25dBHL at octave frequencies from 250 to 4000 Hz. All the participants were native Kannada speakers, with no previous history of otologic and neurological disorders. To rule out cognitive decline, a Mini Mental State Examination (MMSE) (Folstein et al. 1975) was administered on all the participants. All the participants scored >25 on MMSE. An informed consent form was obtained from all the participants. The study was approved by the institutional ethical committee. Signal Processing Spectral Ripple Discrimination Threshold Stimuli with the spectral ripples were created in MATLAB 7 (MATLAB 2004) environment. The method used to create spectral ripples was similar to Won et al. (2007). Twohundred pure-tone frequency components were summed to generate the complex stimuli having the frequencies between 100 Hz to 5000 Hz. The amplitudes of the complex stimulus components were determined by a full-wave rectified sinusoidal envelope on a logarithmic amplitude scale. The ripple peaks were spaced equally on a logarithmic frequency scale. The ripple stimuli were generated adaptively with different densities during threshold tracking. For standard ripples, the phase of the full-wave rectified sinusoidal spectral envelope was created using ‘cos’ function and for reverse ripples, it was ‘sin’ function. Spectrum of standard and reverse ripples are illustrated in Fig. 1. Total duration of the spectral ripple stimuli was 500 ms with 150 ms onset/offset ramping and 5 ms silent intervals were inserted at onset and offset of the stimuli. Speech Recognition in Noise For the sentence recognition task, four lists of sentences were taken from the standardized speech in noise test developed by Methi et al. (2009). All the four lists were of equivalent difficulty as suggested by Methi et al. (2009). Each list contains seven sentences and each sentence has five key words, so a total of 35 keywords are present in each list. The stimuli were recorded on a 32-bit digital acquisition system at
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−80 Standard Reverse Power Spectral Density
−100 −120 −140 −160 −180 −200 −220 0
1000
2000
3000 4000 Frequency (Hz)
5000
6000
Fig. 1 Depicts Spectra of spectral ripple stimulus with standard and reverse spectrum. Continuous and dashed line represents the standard and reverse spectrum respectively. It can noted there that, standard stimuli has the peak in the spectrum where reverse stimuli has dip and vice versa
44,100 Hz sampling frequency. ‘Four-talker babble’ was used as the background noise. Three lists of sentences were presented in noise at the signal to noise ratios (SNR) of +10 dB, 0 dB and -10 dB. One list per SNR was presented and one list was presented in quiet. Root Mean Square (RMS) amplitude of target speech was scaled to achieve the required SNRs. Sentence lists were not randomized across the SNRs. Procedure for Experiment I The experiment was performed on a PC equipped with a Creative Labs SoundBlaster 16 sound card. The participants listened to the stimuli monaurally via Senheiser HD 201 stereo headphones. Ear of presentation was randomized across the participants. Presentation level for all the tasks were between 60-70dBSPL. Spectral Ripple Discrimination Threshold Spectral ripple discrimination threshold (SRDT) was determined using two interval two alternate forced choice, two-up one-down adaptive procedure converging on 70.71 % correct (Levitt 1971). Participants heard two intervals. In one interval, standard and reverse ripples were presented sequentially. In another interval, either two standard ripples or two reverse ripples were presented sequentially. Within in each interval, stimuli were presented with inter-stimulus interval of 100 ms and 250 ms gap was given between the intervals. Participant’s task was to discriminate the ‘standard ripple’ from ‘reverse ripple’. Hence, they were instructed to identify the interval in which two sounds were different. Participants responded by clicking on appropriate button appearing on screen. No feedback was given
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during the test. Test trial started with the two ripples per octave and the ripple density was increased by the ratio of 1.414 after two consecutive positive responses. Ripple density was decreased by the ratio of 1.414 after a negative response. Eight reversals were administered on each participant. Mid points of last six reversals were averaged to obtain SRDT. Before initiating the test, all the participants were given sufficient training. During the training, phase reversal test was conducted at fixed densities of 1 and 1.414 ripple/octave. Feedback was given during the initial phase of training. Training session was considered as complete, when the participants could detect the phase reversal consistently. Speech Recognition in Noise Speech recognition was assessed in open set paradigm for all the four condition (Quiet, +10 dB, 0 dB & -10 dBSNR). ‘Four-talker’ babble was mixed with target sentences. Stimulus mixing and delivery was done using a custom written script in MATLAB. Written responses were taken from each participant. The responses were scored using the ‘loose method’ (Rosen 1992) in which sentence recognition scores were calculated based on the correctly identified key words. Procedure for Experiment II Spectral Ripple Discrimination in Noise First, SRDT was estimated in quiet using the procedure similar to experiment I. Then, standard and reverse ripples were created with the constant ripple density corresponding to the participant’s SRDT in quiet. White noise, band passed filtered between 200 and 5000 Hz was presented along with spectral ripple stimuli to assess the spectral ripple discrimination ability in noise. Noise level was adaptively varied to find out the minimum signal-to-noise (SNR) required for perceiving the phase reversal at the ripple density corresponding to SRDT. In the following sections, this measure will be referred as SNR-SRDT. A 2-AFC, two-down and one-up adaptive procedure was used to estimate the SNR-SRDT (Levitt 1971). Root mean square (RMS) amplitude of the noise was adjusted in 2 dB steps to vary the SNR. Test trial was started with 10 dB SNR. SNR was decreased by 2 dB after two consecutive positive responses and was increased by 2 dB after a negative response. Eight reversals were administered on each participant. Mid points of last six reversals were averaged to obtain SNR-SRDT. Total duration of the band passed noise was 510 ms. Speech Recognition Threshold in Noise Speech recognition threshold in noise was evaluated using the speech in noise test developed by Methi et al. (2009). One list from the original test was selected for the present study. Sentence list contained 7 sentences mixed with the four talker babble at different signal to noise ratios (SNRs). SNR for the first sentence was +20 dB and SNRs were decreased by 5 dB for every sentence. So, the seventh sentence was presented at SNR of -10 dB. Each sentence had 5 key words. The participant’s task was to write down the sentences that they heard. Each correctly identified key word was awarded one point with a total possible score of 35 points. Using the Spearman and
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Karber equation (Finney 1978), speech recognition threshold in noise was estimated by obtaining SNR-50.
Results SRDT The Normality of data was assessed by administering Shapiro-Wilk’s test. The normality test revealed that the SRDT data from both the groups were normally distributed (Older adults, W = 0.885, p > 0.05; young adults, W = 0.918, p > 0.05). Since the data from both the groups were normally distributed, parametric independent t-test was administered to investigate the effect of age on SRDT. Results demonstrated that the younger adults performance was better than older adults (t28 = −12.031, p < 0.05). Figure 2 clearly shows that young adults have better SRDT compared to older adults. Speech Recognition in Noise The speech recognition scores in each condition were calculated by counting the number of correctly identified key words. Speech recognition scores were measured in quiet, +10 dB SNR, 0 dB SNR and -10 dB SNR. Speech recognition scores in quiet and in +10 dB SNR conditions reached ceiling for both the groups. Speech recognition scores at +10 dB, 0 dB & -10 dB SNR was subjected to computation of SNR-50. SNR50 that is, SNR required to obtain the 50 % speech identification scores were calculated using following Spearman and Karber equation (Finney 1978). SNR50 ¼ i þ
1 d correct d − 2 w
In the equation, i = the initial presentation level (dB SNR), d = the attenuation step size (decrement), w = the number of key words per decrement & correct = total number of correctly repeated key words.
SRDT(Ripples/octave)
10 8 6 4 2 0
Elderly
Young Group
Fig. 2 Mean Spectral Ripple Discrimination Threshold (SRDT) for older adults and young adults. Error bar depicts +1 standard deviation
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Independent `t` test revealed that SNR-50 was significantly different between young and older adults (t28 = 12.93, p < 0.05). SNR-50 of young adults was significantly better than older adults. Mean and standard deviation of SNR-50 of both young adults and older adults are represented in Fig. 3. Association between SRDT and Speech Recognition in Noise Pearson’s correlation analysis was administered to investigate the possible association between the SRDT and SNR-50 in older adults. Pearson’s correlation analysis revealed a significant negative correlation between SRDT and SNR-50 (r = −0.46, p < 0.05). Scatter diagram depicting the relationship between ripple discrimination threshold and SNR-50 are represented in Fig. 4. Predictability of SNR-50 from SRDT was investigated using regression analyses with various models. Among all models, linear model could reasonably predict the relationship between SRDT and SNR-50 with marginal significance (F (1, 14) = 3.54, p = 0.08). Linear regression analysis revealed that, SRDT accounted for 21 % of variance in SNR-50. Association between SNR-SRDT and Speech Recognition Threshold in Noise To estimate SNR-SRDT, initially SRDT was obtained without background noise. Mean and standard deviation of SRDT was 3.92 and 0.92 ripple/octave respectively. Background noise was adaptively varied to estimate SNR-SRDT. Mean and standard deviation of SNR-SRDT were 0.44 dB and 0.13 dB respectively. Speech recognition threshold in noise was obtained by estimating SNR-50. Mean and standard deviation of .00
SNR50 (dB)
-2.00
-4.00
-6.00
-8.00 Older Adults
Young Adults
Fig. 3 Mean SNR-50 for older adults and young adults. Error bars represent ±1 standard deviation
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SNR50 = − 0.47*SRDT − 0.072
−0.5
SNR50 (dB)
−1
−1.5
−2
−2.5
−3
−3.5 2
2.5
3
3.5 SRDT (Ripples/Octave)
4
4.5
5
Fig. 4 Scatter plot showing the relationship between SRDT and SNR50 in older adults
SNR-50 were 3.33 dB and 1.02 dB respectively. Pearson’s correlation analysis was performed to investigate the possible association between SNR-50 and SNR-SRDT. There was a significant positive correlation between SNR-50 and SNR-SRDT (r = 0.70, p = 0.011). Quadratic regression analysis revealed that, SNR-SRDT accounted for 63 % of variance in SNR-50 (F (1, 11) = 7.72, p = 0.011). Scatter plot describing the association between SNR-SRDT and SNR-50 is depicted in Fig. 5.
Discussion Primary goal of this study was to determine how many ripples/octave can be resolved by older and younger adults and whether there is any correlation between SRDT and speech perception in noise. Younger adults were able to resolve up to 8 ripples/octaves, whereas older adults were able to resolve only 3.5 ripples/octave. Mean SNR-50 of older adults (−1.70 dB) were significantly poorer than SNR-50 of young adults (−6.48 dB). Results of this study indicate that older adults have poor ability to process spectral information which could be partly leading to poor speech perception in noise. Aging and Spectral Resolution In this study, SRDT method was used to assess spectral resolution in older adults with clinically normal hearing. Older adults had poor spectral resolving ability compared to younger adults despite having normal hearing sensitivity. Previous studies have reported poor spectral resolving abilities in older adults (Clinard et al. 2010; Du et al. 2011; Huang et al. 2010; Lutman and Clark 1986; Patterson et al. 1982; Vander Werff and Burns 2011) and this can be attributed to age related physiological changes in the auditory system. It has been reported that, decline in metabolic processing is the
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5.5
SNR50 = 19.4*SNR SRDT − 12.7*SNR SRDT + 4.82
5
SNR50(dB)
4.5 4 3.5 3 2.5 2 1.5 1 0.2
0.3
0.4
0.5 SNR−SRDT(dB)
0.6
0.7
0.8
Fig. 5 Scatter plot representing the relationship between SNR-SRDT and SNR50 in older adults
predominant cause for poor auditory functioning in older adults (Gates and Mills 2005; Gates et al. 2002). Aging leads to changes in the metabolic activity within the cochlea and the potential sites within the cochlea are inner hair cells, outer hair cells, stria vascularis and afferent spiral ganglion (Schuknecht and Gacek 1993). The Stria vascularis (SV) is responsible for production and maintenance of steady state endocochlear potential (EP). Constant and adequate supply of EP is required for the active processes within the cochlea. Age related changes in cell structures or microvascular disease within the strial vessels or dysfunctional supporting cells might affect strial function (Ohlemiller 2009) leading to loss of EP within the cochlea. This loss would impair the functioning of cochlear amplifier and lead to increased neural threshold (Schmiedt et al. 2002). Furosemide infusion studies on gerbils have shown that, reduced EP is the main cause for auditory dysfunctions in aging auditory system (Schmiedt et al. 2002). During the initial stages, there was reduction in EP but neural thresholds remained within normal limits, which imply that smaller changes in EP may not affect hearing sensitivity. However there is a possibility that, small EP shift may affect complex spectral coding without affecting the hearing sensitivity. There is a lack of strong evidence to suggest that small EP shift affects the complex spectral coding of stimulus such as spectral ripples without affecting detection thresholds. However, future studies are warranted to investigate this hypothesis. Other possible reason for reduced spectral resolution and speech perception ability in aging auditory system could be the diminished inhibitory mechanism at different level of central auditory nervous system (Caspary et al. 1990, 1999; Caspary et al. 2005). When both speech and noise are presented at the same time, the maximum excitation occurs at the characteristic frequency (CF) of the basilar membrane. Growth of intensity at the CF is non-linear compared to adjacent frequencies. When the out-put of the CF is given to other higher centres especially at cochlear nucleus, firing rate at
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CF increases, whereas at other frequencies firing rate is inhibited by releasing inhibitory neurotransmitters. This phenomenon preserves the sharp frequency selectivity and also enables the accurate extraction of formant frequency from the speech by inhibiting the noise (Rhode and Greenberg 1994). Caspary et al. (2005) found that mean discharge rate of dorsal cochlear nucleus of younger rats showed greater non-monotonicity compared to older rats. Authors concluded that this differential discharge rate was due to loss of inhibitory function in cells of dorsal cochlear nucleus. This limitation in inhibitory output in aging individuals may make them to perform poorer in adverse listening conditions. This reduced inhibitory neurotransmission could be one of the possible reasons for older adults in our study to perform poorer on SRDT and speech perception in noise. Association between Spectral Ripple Perception and Speech Recognition in Noise The present research demonstrated a possible relationship between spectral ripple perception and speech recognition in noise. Older adults required larger SNR to understand 50 % of the target speech when compared to young adults. Presence of background noise can alter the spectral cues of the target speech by changing the shape of speech spectrum, reducing the spectral contrast and changing the spectral slope (Assman and Summerfield 2004). As the SNR reduces, reduction in the spectral contrast increases. Individual should have good spectral resolution to identify the target speech when the spectral contrast in the target speech diminishes (Moore and Glasberg 1986). In the current study, spectral resolution was assessed by estimating SRDT using phase reversal test. Individuals with good spectral resolution have large SRDT indicating that, they require minimal spectral contrast to perceive the spectral change. There was a good negative correlation between SRDT and SNR-50 and positive correlation between SNR-SRDT and SNR-50. Since the ability to perceive spectral contrast underlies perception of phase reversal in spectral ripple test and speech perception noise, it is reasonable to assume that speech perception in noise and perception of spectral ripples are related. Thus, it explains the association between SRDTs and SNR-50. Good frequency resolution is also necessary for taking the advantage of spectral separation between noise and speech, while listening to speech in noise (Moore and Peters 1992). Since the older adults had poor spectral resolution as revealed by SRDT, they also had poor speech understanding ability in noise. Clinical Applications of Spectral Ripple Discrimination Test Spectral ripple discrimination test is a relatively new test for measuring spectral resolution and it is yet to reach the clinical platform. Spectral ripple resolution has been shown to correlate well with speech perception in individuals with normal hearing, hearing impairment (Henry et al. 2005) and cochlear implants (Henry et al. 2005; Won et al. 2007). Hence, spectral ripple discrimination test could be valuable when speech testing is not possible. Spectral ripple discrimination test is non-linguistic in nature. Hence, it could be performed on subjects who speak any language. Spectral ripple discrimination test is also proven to be useful in determining the candidacy for cochlear implants (Shim et al. 2014) and amplification devices (Zhang et al. 2013). Peter et al. (2014) studied the effect of maturation on SRDT and considered the potential application of SRDT in auditory processing assessment in children. They reported that SRDT of children with age ranging between 8 and 11 years
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was significantly poorer than SRDT of young adults. However, SRDT of children with age ranging between 12 and 18 years was not significantly different from SRDT of young adults. In the current study, application of spectral ripple discrimination test was extended to older adults. Results of the study revealed that SRDT of older adults was significantly poorer than young adults, and that SRDT could explicate the speech recognition difficulties faced by the older adults. Overall the SRDT can be a valuable clinical tool as it provides valid information about auditory processing abilities and also can serve as the candidacy assessment tool for cochlear implant and amplification devices.
Conclusion The current study investigated the spectral resolving abilities of older adults in order to study the problems faced by the older adults especially in adverse listening conditions. Older adults with clinically normal hearing performed poorer on both spectral resolution measure and speech perception measure compared to younger adults. There was a significant correlation between spectral ripple discrimination threshold and speech perception in noise, which support the fact that understanding speech in adverse listening conditions depends on the ability in resolving spectral cues. Thus, young adults with good spectral resolving ability had better speech perception scores in presence of noise compared to older adults with poor spectral resolving ability. Compliance with Ethical Standards Conflict of Interest
On behalf of all authors, the corresponding author expresses no conflict of interest.
Financial Disclosures organization.
No financial support was received from government or non-government
Informed Consent Informed consent was obtained from all the participants of the study and the study protocol was approved by the institutional ethical committee. Ethical Treatment of Experimental Subjects (Animal and Human) All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional ethics committee, Kasturba medical college (Manipal University), Mangalore and with the 1964 Helsinki declaration and its later amendments.
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