The Role of Age-Related Declines in Subcortical Auditory Processing in Speech Perception in Noise

Older adults, even those without hearing impairment, often experience increased difficulties understanding speech in the presence of background noise. This study examined the role of age-related declines in subcortical auditory processing in the perception of speech in different types of background noise. Participants included normal-hearing young (19 – 29 years) and older (60 – 72 years) adults. Normal hearing was defined as pure-tone thresholds of 25 dB HL or better at octave frequencies from 0.25 to 4 kHz in both ears and at 6 kHz in at least one ear. Speech reception thresholds (SRTs) to sentences were measured in steady-state (SS) and 10-Hz amplitude-modulated (AM) speech-shaped noise, as well as two-talker babble. In addition, click-evoked auditory brainstem responses (ABRs) and envelope following responses (EFRs) in response to the vowel /ɑ/ in quiet, SS, and AM noise were measured. Of primary interest was the relationship between the SRTs and EFRs. SRTs were significantly higher (i.e., worse) by about 1.5 dB for older adults in two-talker babble but not in AM and SS noise. In addition, the EFRs of the older adults were less robust compared to the younger participants in quiet, AM, and SS noise. Both young and older adults showed a “neural masking release,” indicated by a more robust EFR at the trough compared to the peak of the AM masker. The amount of neural masking release did not differ between the two age groups. Variability in SRTs was best accounted for by audiometric thresholds (pure-tone average across 0.5–4 kHz) and not by the EFR in quiet or noise. Aging is thus associated with a degradation of the EFR, both in quiet and noise. However, these declines in subcortical neural speech encoding are not necessarily associated with impaired perception of speech in noise, as measured by the SRT, in normal-hearing older adults

The Role of Age-Related Declines in Subcortical Auditory Processing in Speech Perception in Noise

Older adults, even those without hearing impairment, often experience increased difficulties understanding speech in the presence of background noise. This study examined the role of age-related declines in subcortical auditory processing in the perception of speech in different types of background noise. Participants included normal-hearing young (19 – 29 years) and older (60 – 72 years) adults. Normal hearing was defined as pure-tone thresholds of 25 dB HL or better at octave frequencies from 0.25 to 4 kHz in both ears and at 6 kHz in at least one ear. Speech reception thresholds (SRTs) to sentences were measured in steady-state (SS) and 10-Hz amplitude-modulated (AM) speech-shaped noise, as well as two-talker babble. In addition, click-evoked auditory brainstem responses (ABRs) and envelope following responses (EFRs) in response to the vowel /ɑ/ in quiet, SS, and AM noise were measured. Of primary interest was the relationship between the SRTs and EFRs. SRTs were significantly higher (i.e., worse) by about 1.5 dB for older adults in two-talker babble but not in AM and SS noise. In addition, the EFRs of the older adults were less robust compared to the younger participants in quiet, AM, and SS noise. Both young and older adults showed a “neural masking release,” indicated by a more robust EFR at the trough compared to the peak of the AM masker. The amount of neural masking release did not differ between the two age groups. Variability in SRTs was best accounted for by audiometric thresholds (pure-tone average across 0.5–4 kHz) and not by the EFR in quiet or noise. Aging is thus associated with a degradation of the EFR, both in quiet and noise. However, these declines in subcortical neural speech encoding are not necessarily associated with impaired perception of speech in noise, as measured by the SRT, in normal-hearing older adults

High sentence predictability increases the fluctuating masker benefit

This study examined the effects of sentence predictability and masker modulation type on the fluctuating masker benefit (FMB), the improvement in speech reception thresholds resulting from fluctuations imposed on a steady-state masker. Square-wave modulations resulted in a larger FMB than sinusoidal ones. FMBs were also larger for high compared to low-predictability sentences, indicating that high sentence predictability increases the benefits from glimpses of the target speech in the dips of the fluctuating masker. In addition, sentence predictability appears to have a greater effect on sentence intelligibility when the masker is fluctuating than when it is steady-state

Downscaling Temperature and Precipitation: A Comparison of Regression-Based Methods and Artificial Neural Networks

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18-year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86% and 83% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs). Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34–0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements

Evaluation of the NCEP/NCAR Reanalysis in Terms of Synoptic Scale Phenomea: A Case Study from the Midwestern USA

We evaluate the ability of the National Centers for Environmental Prediction (NCEP)–National Center for Atmosphere Research (NCAR) reanalysis to represent the synoptic-scale climate of the Midwestern USA relative to radiosonde data. Independent, automated synoptic classifications, based on rotated principal component analysis (PCA) of 500 hPa geopotential heights, 850 hPa air temperatures, and 200 hPa wind speeds and a two-step clustering algorithm, result in a 15-type NCEP–NCAR synoptic classification and a 14-type radiosonde classification. The classifications are examined in terms of similarities and differences in the modes of variance manifest in the PCA solutions, the spatial patterns and variability of input variables within each weather type, and the temporal variability of the occurrence of each weather type. The classifications are then compared in terms of these characteristics and the degree of mutual class occupancy. Although the classifications identify a number of the same weather types (in terms of the input data, PCA solution, and mutual occupancy), the correspondence is imperfect. To assess whether the differences in the classifications are due to errant assignment of data to clusters or to differences in the fundamental modes present in the data sets as represented by the PC loadings and scores, a third targeted classification is undertaken that categorizes the NCEP–NCAR reanalysis data according to the radiosonde PCA solution. This classification exhibits a higher degree of similarity to that derived using the radiosonde data (in terms of both interpretability and mutual class occupancy), but the solutions still exhibit considerable differences. It is probable that the discrepancies are partly a function of the differing data structures and densities, but they may also reflect differences in the intensity of synoptic-scale phenomena as manifest in the data sets