The Effect of Age on Sentence Recognition in Noise with Different Noises Across the Adult Lifespan

Abstract

Background/Objectives: The present study examined the effect of age on sentence recognition in noise in different noise conditions among adults with normal hearing sensitivity throughout the adult lifespan. Methods: A total of 113 adults aged between 21 and 65 years participated in the study; based on age, they were categorized into five groups. The sentence recognition was assessed in five noise conditions: speech-shaped noise (SSN), amplitude-modulated speech-shaped noise (AM-SSN), two-male-talker babble (2MB), four-male-talker babble (4MB), and four-female-talker babble (4FB). The sentences were presented at a signal-to-noise ratio of -5 dB in all noise conditions. Results: The sentence recognition scores declined with increasing age in all noise conditions. In addition, age had a differential effect on the sentence recognition scores in the AM-SSN and 2MB conditions compared with the SSN, 4MB, and 4FB conditions. In the AM-SSN and 2MB conditions, the scores were significantly different in the fourth decade compared with young adults. In other noises, the scores were significantly different after 30 years compared with younger adults. Further, across noise conditions, greater scores were obtained in the AM-SSN and 2MB conditions, and the lowest scores were obtained in the 4FB condition. Partial Spearman correlations revealed a moderate-to-strong negative correlation between age and sentence recognition scores across noise conditions. Conclusions: The findings of the present study showed that sentence recognition is negatively affected by age. In addition, age has a differential effect on sentence recognition in different noises.

Keywords: aging; noise; normal hearing; sentence recognition.

Figure 1

Mean hearing threshold (in dB HL) across frequencies and groups for the right ear (panel (A)) and left ear (panel (B)).

Figure 2

Long-term average spectrum of speech-shaped noise, amplitude-modulated speech-shaped noise, two-male-talker babble, four-male-talker babble, and four-female-talker babble used in the present study. It was obtained using the pwelch function in MATLAB, with a Hanning window of 20 ms duration with an overlap of 10 ms and a fast Fourier transform length of 2048 points.

Figure 3

Sentence recognition score across noise conditions and age groups. Circles represent mean scores in speech-shaped noise (○) and amplitude-modulated speech-shaped noise (●), and triangles represent mean scores in two-male-talker babble (▲), four-male-talker babble (△), and four-female-talker babble (▽).

Figure 4

Scatterplot showing the relationship between age and sentence recognition score in speech-shaped noise (panel (A)), amplitude-modulated speech-shaped noise (panel (B)), two-male-talker babble (panel (D)), four-male-talker babble (panel (E)), and four-female-talker babble (panel (G)). Panel (C) shows the improvement in identification scores (masking release) for amplitude-modulated speech-shaped noise relative to the unmodulated condition. Panel (F) shows the difference in performance when the number of talkers was increased from two to four in male-talker babble. Panel (H) shows the difference in performance between the four-male-talker and four-female-talker babble conditions.