NLP and speech intelligibility

Abstract

At some point in our evolutionary history, humans lost vocal membranes and air sacs, representing an unexpected simplification of the vocal apparatus relative to other apes. One hypothesis is that these simplifications represent anatomical adaptations for speech because a simpler larynx provides a suitably stable and tonal vocal source with fewer nonlinear vocal phenomena (NLP). The key assumption that NLP reduce speech intelligibility is indirectly supported by studies of dysphonia, but it has not been experimentally tested. Here, we manipulate NLP in vocal stimuli ranging from single vowels to sentences, showing that the source needs to be stable, but not necessarily tonal. When the task is to discriminate synthesized monophthong and diphthong vowels, continuous NLP (subharmonics, amplitude modulation, and even deterministic chaos) actually improve vowel perception in high-pitched voices, likely because the resulting dense spectrum reveals formant transitions. Rough-sounding voices also remain highly intelligible when continuous NLP are added to recorded words and sentences. In contrast, voicing interruptions and pitch jumps dramatically reduce speech intelligibility, likely by interfering with voicing contrasts and normal intonation. We argue that NLP were not eliminated from the human vocal repertoire as we evolved for speech, but only brought under better control.