Effects of noise on speech production: Acoustic and perceptual analyses

TLDR

Speech production undergoes acoustic changes under adverse conditions such as noise, stress, or cognitive load, influencing speech recognition and motivating research into training, feedback, and robust algorithms. The study analyzed utterances from two speakers in quiet and in 80–100 dB SPL masking noise, measuring acoustic parameters, and conducted perceptual tests comparing intelligibility at equal signal‑to‑noise ratios. Noise increased amplitude, duration, and pitch, altered formant frequencies and short‑term spectra, and produced utterances that were more intelligible than those spoken in quiet, with acoustic differences reliably affecting intelligibility.

Abstract

Acoustical analyses were carried out on a set of utterances produced by two male speakers talking in quiet and in 80, 90, and 100 dB SPL of masking noise. In addition to replicating previous studies demonstrating increases in amplitude, duration, and vocal pitch while talking in noise, these analyses also found reliable differences in the formant frequencies and short-term spectra of vowels. Perceptual experiments were also conducted to assess the intelligibility of utterances produced in quiet and in noise when they were presented at equal S/N ratios for identification. In each experiment, utterances originally produced in noise were found to be more intelligible than utterances produced in the quiet. The results of the acoustic analyses showed clear and consistent differences in the acoustic–phonetic characteristics of speech produced in quiet versus noisy environments. Moreover, these acoustic differences produced reliable effects on intelligibility. The findings are discussed in terms of: (1) the nature of the acoustic changes that take place when speakers produce speech under adverse conditions such as noise, psychological stress, or high cognitive load; (2) the role of training and feedback in controlling and modifying a talker’s speech to improve performance of current speech recognizers; and (3) the development of robust algorithms for recognition of speech in noise.