• unlimited access with print and download
    $ 37 00
  • read full document, no print or download, expires after 72 hours
    $ 4 99
More info
Unlimited access including download and printing, plus availability for reading and annotating in your in your Udini library.
  • Access to this article in your Udini library for 72 hours from purchase.
  • The article will not be available for download or print.
  • Upgrade to the full version of this document at a reduced price.
  • Your trial access payment is credited when purchasing the full version.
Buy
Continue searching

Speech-identification performance of older adults in a competing-talker background: Effect of fundamental frequency and sentence onset differences

ProQuest Dissertations and Theses, 2009
Dissertation
Author: Jae Hee Lee
Abstract:
The purpose of the current study was to investigate the benefits of differences in fundamental frequency (F0) and temporal onset between sentence pairs in four listener groups differing in age and hearing sensitivity. The four groups were: (1) young normal-hearing (YNH) adults; (2) elderly normal-hearing (ENH) adults; (3) elderly hearing-impaired (EHI) adults; and (4) young normal-hearing adults with noise masking (YNM) simulating the hearing loss of the EHI group. Listeners heard two competing sentences separated by F0, temporal onset, or by the combination of both cues. With the increase of F0 or onset separation, speech-identification performance improved in all groups. Differences in F0 and onset between concurrent sentences appeared to substantially reduce the identification errors primarily by reducing the confusion with the competing message. The listener groups did not differ regarding their ability to identify which of the two sentences was the target sentence. For the identification task, overall, the elderly listeners received less benefit from F0 difference and onset asynchrony compared to the young listeners. Given that the EHI listeners performed significantly worse than the young groups (YNH, YNM), but no other groups differed significantly, the combined effects of aging and cochlear pathology appeared to affect competing-speech identification, rather than either aging or reduced audibility alone. Elderly listeners' benefits from F0 differences were significantly correlated with high-frequency thresholds and age whereas elderly listeners' benefits from onset asynchrony were significantly correlated with high-frequency thresholds and education level. A general sentence-identification ability of the elderly listeners was also predicted by both high-frequency thresholds and the modality-aspecific, cognitive-related variables, such as education level. This indicates that both age-related peripheral deficits and age-related cognitive deficits contribute to older adults' difficulty understanding the target message in the presence of a distracter message, even though the audibility of the two messages has been restored.

Table of Contents

Acknowledgements……………………………........................................................................... iv

Abstract…………………………………………………………….…………………………… vii

Table of Contents…………………………………………………………………………………ix

List of Figures……………………………………………………………………………...……..xi

List of Tables…………………………………………………………………………………….xiv

List of Appendices..……………………………………………………………………………..xvi

Chapter 1. Introduction…………………………………………………………………………... 1

Chapter 2. Background literature review..………………………………………………………...5

Perceptual benefit from F0 differences (ΔF0) between competing speech signals………….……………………………………………………………12

Perceptual benefit from onset asynchrony between competing speech signals …………….…………………………………………………………………34

Chapter 3. Methods……………………………………………………………………………... 42

Participants………………………………………………………………… 42

Stimuli……………………………………………………………………... 46

Calibration and apparatus………………………………………………..….51

Procedures………………………………………………………………….. 55

Scoring and data analysis………………………………………………….. 61

Scoring for the detection task ……….…………………………..62

Scoring for the identification task ……….…………………….. 63

Data analysis …………………………….…………………….. 64

Chapter 4. Results………………………………………………………………………………..66

Detection sensitivity (d’) and response bias (β) measures……….………. 66

x

Percentage correct color-number identification…………………………... 72

Percentage of intrusions among incorrect responses…………………….....78

Chapter 5. Discussion…………………………………………………………………………... 85

Group data ……………………………………………………………….. 86

Detection and response bias measures ….………………….….. 86

Accuracy and error in color-number identification…………….. 87

Individual data ………….……...…..…..……….…….………………......92

Individual differences in F0 and onset benefits for d’, color- number score, and intrusion………........…….………………....93

Relation between subject-related predictors and F0 and onset benefits of individuals………..……....…….……………….......98

Is there a general speech-identification ability?…………….....102

Relation between subject-related predictors and general speech- identification ability of the elderly individuals…......................105

Chapter 6. Conclusions…………………………………………………………………………110

xi

List of Figures

Figure 1: Median hearing loss of 20-, 40-, 60-, and 80-year-old males, adapted from Humes (2008).………………………………………………………..…………….……….…...7

Figure 2: Median hearing loss of 60- and 80-year-old males from Figure 1, with speech sounds presented at typical conversational levels (60-65 dB SPL, represented orthographically) that are superimposed on the audiogram, adapted from Humes (2008)……….….…….8

Figure 3: Hypothetical data for correlational research designs. Top two panels are for speech in quiet or noise, plotted as a function of hearing loss (left) or age (right). Bottom two panels are for temporally degraded speech, plotted as a function of hearing loss (left) or age (right), adapted from Humes (2008).……………………………………….……...10

Figure 4: Schematic magnitude spectrum of mixtures of two different vowels, /i/ and /a/, with different F0s of 100 Hz and 140 Hz (top) and with the same F0 of 100 Hz (bottom), adapted from Darwin (2008).……………………….…….…………………….……...24

Figure 5: Relative amplitude spectrum of a mixture of 125-Hz pure-tone and 128-Hz pure-tone (top), of 125-Hz and 200-Hz pure-tone (center), and of 125-Hz and 1000-Hz pure-tone (bottom panel) ………………………………………...….…………………….……...27

Figure 6: (a) neural cancellation filter input of 100-Hz half-wave rectified sinewave for a single vowel with F0 of 100 Hz; (b) neural cancellation filter output of (a); (c) neural cancellation filter input of half-wave rectified sum of two sines, 80-Hz and 100-Hz for double vowels (differing in amplitude by 10 dB); and (d) neural cancellation filter output of (c), adapted from de Cheveigné (1997) ………...….……………………......30

Figure 7: The natural F0 contour of a CRM sentence when unprocessed (×) or up-shifted by ∆F0 of 6 ST (unfilled circle).…………………………..……… …………………………..50

Figure 8: Relative amplitude spectra of concatenated CRM wave files with or without spectral shaping...……………………………..…………..……………..... …………………...52

Figure 9: Sensation level of the CRM speech signals presented to the 15 ENH listeners (unfilled circles), the 10 EHI listeners (filled triangles) tested at 85 dB SPL, and the 5 EHI listeners (unfilled triangles) tested at 91 dB SPL. The dashed line shows 15-dB sensation level (SL) displaying the optimal or asymptotic band sensation level according to the speech intelligibility index (SII) (ANSI, 1997) and the dotted line shows the objective of at least 10 dB SL for this study.…………........…………………………...54

Figure 10: Waveforms of sample CRM sentence pairs without F0 and onset differences between sentences (top panels), with ΔF0 of 6 ST (2 nd row of panels), onset-asynchrony of 600 ms alone (3 rd row of panels), or both ΔF0 of 6 ST and 600-ms onset-asynchrony were provided (bottom panels)..……………………………..…………..……………......... 56

xii

Figure 11: Graphical interface on the touch screen for detection and identification tasks………60

Figure 12: Means and standard errors of detection sensitivity (d’) to call sign ‘Baron’ as a function of onset asynchrony for the four listener groups (YNH = Young normal- hearing; ENH = Elderly normal-hearing; EHI = Elderly hearing-impaired; and YNM = Young normal-masked)…………………………………………………….………67

Figure 13: Means and standard errors of beta, β, on the detection response of ‘Baron’ as a function of onset asynchrony for the four listener groups (YNH = Young normal- hearing; ENH = Elderly normal-hearing; EHI = Elderly hearing-impaired; and YNM = Young normal-masked).……...……………………………………………….……70

Figure 14: Means and standard errors of percent correct color-number identification as a function of onset asynchrony for the four listener groups (YNH = Young normal-hearing; ENH = Elderly normal-hearing; EHI = Elderly hearing-impaired; and YNM = Young normal-masked)..………………………………………………..……………………73

Figure 15: Effect of ΔF0 and onset asynchrony for each of the four listener groups (YNH = Young normal-hearing; ENH = Elderly normal-hearing; EHI = Elderly hearing- impaired; and YNM = Young normal-masked)……………………………...………77

Figure 16: Means and standard errors in the percentage of intrusions among the incorrect responses across the four groups. The possible proportion of intrusion selected by chance, 52%, is shown by a horizontal dotted line....………………………………...81

Figure 17: The degree of benefit from ΔF0 of 3 ST (left) and 6 ST (right) on transformed percent -correct color-number (CN) identification in RAU plotted for each listener group. Mean ΔF0 benefit of each group displayed with a filled circles and the individual ΔF0 benefits (N = 15 per group) with open circles.………………………………….……94

Figure 18: The amount of benefit from 50 ms of onset asynchrony (left) and 600 ms of onset separation (right) for the same ΔF0 on transformed percent-correct color-number (CN) identification in RAU plotted for each listener group. Mean onset benefit of each group displayed with filled circles and the individuals’ onset benefits (N = 15 per group) with open circles…………...………………….…….………………….……..95

Figure 19: Scatter plot of the onset asynchrony benefit of 600 ms as a function of ΔF0 benefit of 6 ST for the young (N = 30, open triangles) and old individuals (N = 30, filled circles). Individual benefit from F0 and onset separation is illustrated in transformed percent- correct color-number (CN) identification in RAU……………………………..…….97

Figure 20: Scatter plot of ΔF0 benefits of 30 elderly individuals (15 ENH: unfilled circles, 15 EHI: filled circles) from 6-ST shift (left panels) or from a 600 ms onset asynchrony (right panels) as a function of the three subject variables: high-frequency pure-tone threshold averaged across 1, 2, 4 kHz labeled as HFPTA (1, 2, 4 kHz) (top panels), age in years (center panels), and education level (bottom panels). The Pearson

xiii

correlation coefficient r (for HFPTA (1, 2, 4 kHz) and age) or Spearman correlation coefficient rs (for education level) for each variable is shown in each panel………100

Figure 21: Scatter plot of color-number (CN) identification scores at ΔF0 of 6 ST (left panels) and at 600-ms asynchrony (right panels) as a function of score at 0 ST, 0 ms condition, for the YNH (top), ENH (2 nd ), EHI (3 rd ), and YNM (bottom) listeners...…...……...104

Figure 22: Scatter plot of 30 elderly individuals’ principal component factor scores for color- number identification (labeled as CN identification factor score). Individual CN (color-number) identification factor scores were plotted as a function of HFPTA (1, 2, 4 kHz) (top panel), year of education (2 nd panel), and of digit span combined (bottom panel). The solid lines display the best-fit linear regression.............................…… 107

xiv

Lists of Tables

Table I: Demographic information (i.e., age, scores of MMSE and digit-span, and air-conducted audiometric thresholds) of individual ENH and EHI listeners (ENH = the elderly normal-hearing; EHI = the elderly hearing-impaired; MMSE = Mini-Mental Status Exam; PTA 1,2,4 or PTA 0.5,1,2 = Pure-tone Thresholds Averaged across 1, 2, 4 kHz or across 0.5, 1, 2 kHz). ………………………………………………………………..….44

Table 2: Mean (standard deviation) F0 values in Hz for each of the eight talkers, obtained from the six same CRM sentences.…………………………………………………………...47

Table 3: Results of the 4 (group) × 3 (ΔF0) × 5 (onset asynchrony, ΔOnset) ANOVA for d’ across the four listener groups, with a between-subjects factor of group and two within-subjects factors of F0 and onset (* significant at p < 0.05; ** significant at p < 0.01)...……..…68

Table 4: Summary of the 4 (group) × 3 (ΔF0) × 5 (onset asynchrony, ΔOnset) ANOVA results for β across the four listener groups, with a between-subjects factor of group and two within-subjects factors of F0 and onset (* significant at p < 0.05; ** significant at p < 0.01)...…………………………………………………………………..………………71

Table 5: Results of the 4 (group) × 3 (ΔF0) × 5 (onset asynchrony, ΔOnset) ANOVA for the correct color-number identification score (in RAU) across the four listener groups, with a between-subjects factor of group and two within-subjects factors of F0 and onset (* significant at p < 0.05; ** significant at p < 0.01)...………………………………..…..74

Table 6: Results of the 3 (ΔF0) × 5 (onset asynchrony, ΔOnset) repeated-measures ANOVA for the color-number identification score (in RAU) of each group, with two within-subjects factors of F0 and onset (* significant at p < 0.05; ** significant at p < 0.01). Each cell in the table contains an F value, degrees of freedom (in parenthesis), and partial eta- squared……………………………………………………………………………...…..76

Table 7: Summary of the post-hoc univariate ANOVA results and follow-up t-tests for groups within each condition, using the correct color-number identification score (in RAU). Only the significantly different paired-comparisons are listed………………………....79

Table 8: Results from the 4 (group) × 3 (ΔF0) × 5 (onset asynchrony, ΔOnset) ANOVA for the intrusion proportions across the four listener groups, with a between-subjects factor of group and two within-subjects factors of ΔF0 and onset asynchrony (* significant at p < 0.05; ** significant at p < 0.01)...…………………………………………………...…82

Table 9: Summary of Pearson product-moment correlation results across 15 conditions for correct color-number (CN) scores in RAU, either across all 4 groups (2 nd row) or for each group (from 3 rd to 6 th rows). The number of pairwise correlations with significant (p < 0.05) relationships across 15 conditions is counted and the range of correlation coefficient (r) values is reported in square brackets. Note that 108 pairwise correlations would be the maximum number of significant correlations possible for 15 conditions

xv

………….…………………………………………………………………………… 103

xvi

Lists of Appendices

Appendix A: Written instructions on the detection and identification tasks presented to the listener.………………………………………………………………………...…125

1

Chapter 1. Introduction

Understanding a spoken message in the presence of background noise or competing speech is a common listening problem for many older adults. The recognition process in the presence of competing signals is naturally complicated since it includes the encoding of each input signal at the peripheral level, followed by processing of the central auditory and cognitive systems. When age-related internal changes occur in peripheral, central, and general cognitive functions, any one of these changes can be sufficient to affect the understanding of competing speech signals, but older adults often experience changes at multiple levels of processing. A difference in the fundamental frequency (F0) or onset time between target and competing speech signals facilitates the listeners’ ability to separate the target message from the interfering message, consequently enhancing identification accuracy for the target. A number of previous studies (reviewed in Chapter 2), most often using concurrent vowels and young normal-hearing listeners, have consistently found a robust contribution of differences in F0 (ΔF0) or in onset time. Given the possible peripheral, central-auditory, and cognitive changes occurring with advancing age, generalizing these findings to sentence-level stimuli and older

2 listeners with near-normal or impaired hearing sensitivity requires direct evaluation. The present study aimed to investigate the benefit from a change in fundamental frequency (ΔF0) and onset asynchrony between sentence pairs on the speech-identification performance of four listener groups (young and old listeners with or without reduced audibility). In the current study, two different sentences spoken by the same male speaker were manipulated to produce differences in F0 or onset asynchrony. The primary interest was in the identification accuracy for target words in the competing sentences. However, because the target sentence in each sentence pair was indicated via an acoustic cue that was lexical in nature, it was also necessary to measure the listeners’ ability to detect this cue. The following four research questions were addressed in this study. First, would F0 difference and onset asynchrony between sentences comprising each sentence pair improve the ability to detect and identify target words when both sentences are audible? Second, how do the ΔF0 and onset-asynchrony cues interact in the speech-detection and speech-identification performance for four listener groups differing in age and high- frequency audibility? Third, if performance differences exist between young and older adults, are these differences related to the effects of peripheral or cognitive deficits in the ability to make use of F0 differences and onset asynchrony? Fourth, what is the nature of

3 incorrect identification responses, and do the types of errors differ across the four listener groups? For example, are errors due to an inability to hear the acoustic cue marking the target sentence, an ability to hear the target sentence itself, or an inability to ignore the competing message? Clearly, if normal conversational speech levels (~65 dB SPL) are used, the peripheral hearing loss of the older adults will result in decreased identification performance for the target sentence (see Chapter 2). This would be a relatively uninteresting set of measurements. What would be expected, however, if the speech stimuli were spectrally shaped like well-fit amplification? Would the elimination of inaudibility through such amplification result in equivalent performance by young and older adults or would age-related peripheral pathology, central-auditory deficits, or cognitive dysfunction still result in diminished performance in the older adults? Further, could such deficits be alleviated by enhancing the acoustic cues, such as F0 differences or onset asynchrony, for the older adults? To address these questions in this study, four groups of subjects listened to competing sentence pairs that were spectrally shaped to eliminate the contributions of stimulus inaudibility. The four groups selected here were: (1) young normal-hearing adults; (2) elderly normal-hearing adults; (3) elderly hearing-impaired adults; and (4)

4 young normal-hearing adults with noise masking. The noise masking made use of spectrally shaped noise to simulate the inaudibility of high frequencies experienced by the elderly adults with impaired hearing. By comparing the performance of various groups it will be possible to examine the effects of age, high-frequency hearing loss, or the combination, on performance in the competing-speech task.

5 Chapter 2. Background literature review

As listeners become older, age-related internal changes can negatively affect both the hearing sensitivity responsible for the peripheral encoding of input signals and processing of speech by the central-auditory or cognitive systems (CHABA, 1988). When the age-related peripheral deficit in hearing sensitivity, often referred to as presbycusis, is the sole deficit the listener’s understanding of the target message is primarily affected by the inaudibility of the target speech arising from the presence of cochlear pathology (Amos & Humes, 2007; Humes & Roberts, 1990; Humes et al., 1994, Sommers, 1997; van Rooij & Plomp, 1992). Because this age-related hearing loss rarely results in total deafness, most of older adults with presbycusis can still hear speech, but often have difficulty understanding speech. Given the normal involvement of processing at peripheral, central-auditory, and cognitive levels in speech understanding, three hypotheses (i.e., auditory peripheral hypothesis, central-auditory hypothesis, and general cognitive hypothesis) have been used to explain the speech-understanding problems of the elderly adults with impaired hearing (CHABA, 1988; Humes, 1996). Age-related structural and functional changes in the auditory periphery have been

6 assumed to directly affect the audibility and processing of speech sounds in the peripheral hypothesis. The age-related peripheral hearing loss often starts in the high frequencies, with a progressive deterioration at high frequencies that is faster than at low frequencies. Figure 1 displays median pure-tone hearing threshold values in males with various ages from an international standard (ISO, 2000). As shown, 80-year-old males have a greater loss of hearing sensitivity at higher frequencies, especially at frequencies from 2000-8000 Hz, compared to 20, 40, and 60-year-old males. Due to the high-frequency hearing loss, some high-frequency, low-intensity speech sounds, usually consonantal sounds, can become inaudible even when a single talker speaks at a normal conversational level in quiet conditions. Figure 2 from Humes (2008) depicts how the high-frequency hearing loss of 60 or 80-year-old males (Figure 1) can negatively affect the audibility of some consonants that are presented at typical conversational levels (60-65 dB SPL). Especially the consonants, such as “t, f, s, th” displayed in Figure 2, can be partially or completely inaudible for older individuals. For example, they may hear words such as “ton”, “fun”, and “son” as “un”, resulting in a speech-understanding problem. Many previous studies (e.g., Divenyi & Haupt, 1997; Gordon-Salant & Fitzgibbons, 1997; Humes, 1996, 2002; Humes & Roberts, 1990; van Rooij & Plomp,

7

Figure 1. Median hearing loss of 20-, 40-, 60-, and 80-year-old males, adapted from Humes (2008).

8

Figure 2. Median hearing loss of 60- and 80-year-old males from Figure 1, with speech sounds presented at typical conversational levels (60-65 dB SPL, represented orthographically) that are superimposed on the audiogram, adapted from Humes (2008).

9 1992) have consistently found that older individuals’ speech-understanding performance is well-explained by the audibility of speech, estimated from hearing thresholds, when a single talker speaks in quiet or in a steady-state background noise. However, when a target speech signal is presented with a background noise that fluctuates like speech or with competing speech, individual differences in the speech-understanding performance of older individuals is not predicted by audibility alone. Rather, age-related deficits often emerge as primary or strong secondary predictors (e.g., Humes, 2002, 2007). Different hypothetical patterns of results for different listening situations are demonstrated in Figure 3 from Humes (2008). The top two panels of Figure 3 show that the percentage of correctly identified words is correlated with high-frequency hearing thresholds (top left), but not with age (top right) when listening to a single talker in quiet or in a steady-state background noise. The bottom panels, however, show speech- understanding scores when the target speech is temporally degraded or presented in competing speech (or a speech-like fluctuating noise). As seen in the bottom panels of Figure 3, word scores are better predicted by age (bottom right) than by high-frequency hearing loss (bottom left), indicating the presence of age-related deficits in temporally degraded or competing speech listening environments. The role of age-related deficits in central-auditory and general cognitive process

10

Figure 3. Hypothetical data for correlational research designs. Top two panels are for speech in quiet or noise, plotted as a function of hearing loss (left) or age (right). Bottom two panels are for temporally degraded speech, plotted as a function of hearing loss (left) or age (right), adapted from Humes (2008).

11 has also been confirmed in recent studies (Amos & Humes, 2007; Coughlin, 2004; George et al., 2006, 2007; Humes, 2002, 2007; Humes, Lee, & Coughlin, 2006). Once the reduced speech audibility of the target message has been alleviated by clinical amplification or laboratory spectral shaping, other factors associated with central and cognitive deficits emerge, especially while listening to speech with competing speech in the background. When the target and the interfering messages are presented concurrently, listeners are required to encode and contrast spectral and temporal features of both competing speech signals, segregate the target from the competing source, and use a cognitive strategy to selectively attend to the target message while inhibiting the competing information. These processes may be suprathreshold peripheral, central-auditory, or cognitive in nature. Although age-related vascular, metabolic, or other systemic factors can cause detrimental changes to the aforementioned processes, various acoustic external factors can also impact the auditory segregation of collocated competing signals. Acoustical features of the competing speech messages, such as fundamental frequency and corresponding harmonics, onset time, intensity, frequency, total duration, and spatial location, can facilitate the segregation of competing speech signals. Onset asynchrony and ΔF0 are potentially powerful temporal segregation cues when processing monaurally

12 presented competing speech signals (Bregman, 1990; Darwin, 2001; Hedrick & Madix, 2009; Lentz & Marsh, 2006). Information from ΔF0 is carried by the temporal fine structure of the auditory nerve firings, which determines the pitch of each competing voice. The temporal onset asynchrony between two signals is an important gross temporal cue extracted by the temporal envelope of two sounds. Because two different speakers usually have different F0 values and often do not start speaking at the same time, these are two commonly occurring sound-segregation cues available to the listeners. As a result, it is important to examine the role of ΔF0 and onset asynchrony between two competing speech signals and any negative impact of aging on the use of these two sound-segregation cues. This chapter reviews previous studies on the perceptual benefits of ΔF0 and onset asynchrony for speech understanding in the presence of competing speech. In addition, studies examining differences in performance among older adults regarding ΔF0 or onset- asynchrony benefits are reviewed.

1. Perceptual benefit from F0 differences (ΔF0) between competing speech signals The fundamental frequency, F0, is defined as the frequency at which the vocal folds vibrate when voiced speech sounds are made. The vibration generates a periodic

13 fluctuation of air pressure such that F0 is calculated based on the number of vibrations per second and is generally expressed in units of Hertz (Hz). F0 is inversely proportional to the vibrating mass and directly proportional to the tension (stiffness) of the vocal folds. Depending on the features of mass or tension, a slow or fast vibration of vocal folds yields a low or high F0 value, consequently eliciting a low- or high-pitched sound. For example, the F0 value of men is typically an octave lower than that of women due to the longer and heavier vocal folds, eliciting a low-pitch sensation [e.g., F0 of 132 Hz for men and 224 Hz for women, reported by Peterson & Barney (1952)]. F0 can be raised when a speaker increases tension to the vocal folds. The F0 value conveys various cues at subsegmental, segmental, and suprasegmental levels, such as acoustic cues to vowel identity (different F0 and formant frequencies across vowels), gender (lower F0 in men), age (a decrease in mean F0 with age, more pronounced in women), intonation (greater F0 fluctuations for a greater change in intonation), and the speaker’s emotional state (higher F0 in ‘happy’ or lower F0 in ‘sad’ emotional state) (Cooper & Sorensen, 1981; Gelfer & Mikos, 2005; Harrington et al., 2007; Murray & Arnott, 1993; Peterson & Barney, 1952). When listeners simultaneously hear two speech signals with different F0s, it is easier to separate one from the other sound source. There is ample evidence that F0 separation remarkably improves performance for concurrent speech sounds overlapping

14 in frequency and time, whether the competing speech signals are steady-state synthesized vowel pairs (Alain et al., 2005; Arehart et al., 1997, 2005; Assmann & Summerfield, 1990, 1994; Chalikia & Bregman, 1989; Culling & Darwin, 1993, 1994; de Cheveigné, 1997; Meddis & Hewitt, 1992; Rossi-Katz & Arehart, 2005; Stubbs & Summerfield, 1988; Summerfield & Assmann, 1989, 1991; Summers & Leek, 1998; Vongpaisal & Pichora-Fuller, 2007), non-sense syllables (Vestergaard et al., 2009), sentence pairs without natural F0 variation (Assmann, 1999; Bird & Darwin, 1998; Brokx & Nooteboom, 1982) or with natural F0 variation preserved (Assmann, 1999; Darwin et al., 2003; Oxenham & Simonson, 2009; Summers & Leek, 1998). A doubling of sound frequency is known as an octave. The octave interval is divided into twelve semitones, which results in one semitone corresponding to the 12 th

Full document contains 148 pages
Abstract: The purpose of the current study was to investigate the benefits of differences in fundamental frequency (F0) and temporal onset between sentence pairs in four listener groups differing in age and hearing sensitivity. The four groups were: (1) young normal-hearing (YNH) adults; (2) elderly normal-hearing (ENH) adults; (3) elderly hearing-impaired (EHI) adults; and (4) young normal-hearing adults with noise masking (YNM) simulating the hearing loss of the EHI group. Listeners heard two competing sentences separated by F0, temporal onset, or by the combination of both cues. With the increase of F0 or onset separation, speech-identification performance improved in all groups. Differences in F0 and onset between concurrent sentences appeared to substantially reduce the identification errors primarily by reducing the confusion with the competing message. The listener groups did not differ regarding their ability to identify which of the two sentences was the target sentence. For the identification task, overall, the elderly listeners received less benefit from F0 difference and onset asynchrony compared to the young listeners. Given that the EHI listeners performed significantly worse than the young groups (YNH, YNM), but no other groups differed significantly, the combined effects of aging and cochlear pathology appeared to affect competing-speech identification, rather than either aging or reduced audibility alone. Elderly listeners' benefits from F0 differences were significantly correlated with high-frequency thresholds and age whereas elderly listeners' benefits from onset asynchrony were significantly correlated with high-frequency thresholds and education level. A general sentence-identification ability of the elderly listeners was also predicted by both high-frequency thresholds and the modality-aspecific, cognitive-related variables, such as education level. This indicates that both age-related peripheral deficits and age-related cognitive deficits contribute to older adults' difficulty understanding the target message in the presence of a distracter message, even though the audibility of the two messages has been restored.