Phoneme and Word Scoring in Speech-in-Noise Audiometry

Characterizing correlations in partial credit speech recognition scoring with beta-binomial distributions

Partial credit scoring for speech recognition tasks can improve measurement precision. However, assessing the magnitude of this improvement with partial credit scoring is challenging because meaningful speech contains contextual cues, which create correlations between the probabilities of correctly identifying each token in a stimulus. Here, beta-binomial distributions were used to estimate recognition accuracy and intraclass correlation for phonemes in words and words in sentences in listeners with cochlear implants (N = 20). Estimates demonstrated substantial intraclass correlation in recognition accuracy within stimuli. These correlations were invariant across individuals. Intraclass correlations should be addressed in power analysis of partial credit scoring.

Speech-in-Noise Testing: An Introduction for Audiologists

Speech-in-noise testing has been proposed as a useful part of the audiometric test battery dating back to the earliest years of the field of audiology. Many speech-in-noise tests have been developed and used to varying degrees. However, multiple barriers have prevented speech-in-noise testing from being used widely in the clinic. The purpose of this article is to provide a resource to audiologists and other hearing health professionals who want to know (1) what tests are available for use, (2) the rationale behind specific tests, and (3) important considerations when selecting one or more tests to use clinically. In addition, data are presented for four speech-in-noise tests with the purpose of comparing outcomes as a function of age and hearing status. The four tests (QuickSIN, Words in Noise [WIN], Listening in Spatialized Noise-Sentences [LiSN-S], and Coordinate Response Measure [CRM]) were completed by 30 individuals from three groups: 10 young adults with normal hearing, 10 older adults with normal hearing, and 10 older adults with hearing loss. The results suggest that, despite significant differences in performance between groups, group overlap was present such that some individuals from one group performed similar to some individuals of other groups; therefore, individual performance was more important than associated group. When selecting an appropriate speech-in-noise test to use clinically, audiologists should carefully consider the purpose of their testing and the type of information they desire as an outcome. A quick-resource table and appendix is provided to aid audiologists and other health professionals in their selection of an appropriate speech-in-noise test.

Aging and Speech-in-Noise Perception

The objective was investigating the effect of age on speech-in-noise perception (SINP) using word perception score in white noise (WPS in WN). This cross-sectional study was conducted on 76 participants, including 30 elderly (older than 61 years) and 46 young adults (between 14 and 35 years) with normal levels of stress, night sleep and mini-mental states. Audiological evaluations included acoustic immittance testing, pure tone audiometry, determination of speech reception threshold and WPS in WN. Data analysis were performed using Mann-Whitney and Tukey HSD tests. Based on the results of the tests, the participants were divided into three groups: (1) young adults with normal hearing (n = 30), (2) elderly adults with normal hearing (n = 16), (3) elderly adults with mild to moderate high frequency hearing loss (n = 14). In both groups of old adults, the means WPS in WN differences were significant only in the left ears (Pv = 0.008, 0.033, 0.025 for SNR = 0, +5, +10 dB). In the three groups and in the right ears, there were the significant differences between the means of WPS in WN (Pv = 0.002, 0.000, 0.001 for SNR = 0, +5, +10 dB), and also the left ears (Pv = 0.000, 0.002, 0.002 for SNR = 0,+5, +10 dB).There is a relationship between increasing age and decreasing WPS in WN. The deleterious effects of aging on SINP decline are greater than that of hearing loss.

Rates, Indications, and Speech Perception Outcomes of Revision Cochlear Implantations

Revision cochlear implant (RCI) is a growing burden on cochlear implant programs. While reports on RCI rate are frequent, outcome measures are limited. The objectives of the current study were to: (1) evaluate RCI rate, (2) classify indications, (3) delineate the pre-RCI clinical course, and (4) measure surgical and speech perception outcomes, in a large cohort of patients implanted in a tertiary referral center between 1989–2018. Retrospective data review was performed and included patient demographics, medical records, and audiologic outcomes. Results indicated that RCI rate was 11.7% (172/1465), with a trend of increased RCI load over the years. The main indications for RCI were device-related failures (soft-45.4%, hard-23.8%), medical failure (14%), trauma (8.1%), and surgical failure (6.4%). Success rate was 98.8%. Children comprised 78% (134) of the cohort and were more likely than adults to undergo RCI. Most (70%) of the RCIs were performed within 10 years from primary implantation. Speech perception outcome analysis revealed unchanged or improved performance in 85% of the cases and declined performance in 15%. Current findings confirm that RCI is a safe with high clinical efficacy; however, the non-negligible percentage of patients that exhibited declined performance post-RCI should be considered in decision-making processes regarding RCI. Routine follow-up during their first years post-implantation is warranted.

Association Between Tinnitus Pitch and Consonant Recognition in Noise

Purpose Difficulties in speech-in-noise understanding are often reported in individuals with tinnitus. Building on our previous findings that speech-in-noise performance is correlated with subjective loudness of tinnitus, this study aimed to investigate the effect of tinnitus pitch on consonant recognition in noise. Method Pure-tone audiometry and the Quick Speech-in-Noise Test were conducted on 66 participants categorized into four groups by their hearing sensitivity and self-report of tinnitus. Consonant recognition scores at various frequency ranges were obtained at the 5 dB SNR condition of the Quick Speech-in-Noise Test. Participants with tinnitus also completed a tinnitus pitch-matching procedure. Correlation analyses were conducted between tinnitus pitch and the frequency of the worst consonant recognition, and the error rates based on word and sentence position were compared. Results Regardless of hearing sensitivity, tinnitus pitch did not correlate with the frequency of the worst consonant recognition. Sentence-initial word recognition was affected by hearing loss, whereas sentence-final word recognition was not affected by hearing loss or tinnitus. In contrast to individuals with normal hearing, participants with hearing loss varied in full-sentence recognition, with those reporting tinnitus exhibiting significantly higher error rates. Conclusions The findings suggest that the effect of tinnitus on consonant recognition in noise may involve higher level functions more than perceptual characteristics of tinnitus. Furthermore, for individuals with speech-in-noise concerns, clinical evaluation should address both hearing sensitivity and the presence of tinnitus. Future speech-in-noise studies should incorporate cognitive tests and, possibly, brain imaging to parse out the contribution of cognitive factors, such as cognitive control, in speech-in-noise in tinnitus.

Bayesian Applications in Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592.

A perspective on brain-behavior relationships and effects of age and hearing using speech-in-noise stimuli

Understanding speech in background noise is often more difficult for individuals who are older and have hearing impairment than for younger, normal-hearing individuals. In fact, speech-understanding abilities among older individuals with hearing impairment varies greatly. Researchers have hypothesized that some of that variability can be explained by how the brain encodes speech signals in the presence of noise, and that brain measures may be useful for predicting behavioral performance in difficult-to-test patients. In a series of experiments, we have explored the effects of age and hearing impairment in both brain and behavioral domains with the goal of using brain measures to improve our understanding of speech-in-noise difficulties. The behavioral measures examined showed effect sizes for hearing impairment that were 6-10 dB larger than the effects of age when tested in steady-state noise, whereas electrophysiological age effects were similar in magnitude to those of hearing impairment. Both age and hearing status influence neural responses to speech as well as speech understanding in background noise. These effects can in turn be modulated by other factors, such as the characteristics of the background noise itself. Finally, the use of electrophysiology to predict performance on receptive speech-in-noise tasks holds promise, demonstrating root-mean-square prediction errors as small as 1-2 dB. An important next step in this field of inquiry is to sample the aging and hearing impairment variables continuously (rather than categorically) - across the whole lifespan and audiogram - to improve effect estimates.