Factors modulating perception and production of speech by AI tools: a test case of Amazon Alexa and Polly

To develop AI tools that can communicate on par with human speakers and listeners, we need a deeper understanding of the factors that affect their perception and production of spoken language. Thus, the goal of this study was to examine to what extent two AI tools, Amazon Alexa and Polly, are impacted by factors that are known to modulate speech perception and production in humans. In particular, we examined the role of lexical (word frequency, phonological neighborhood density) and stylistic (speaking rate) factors. In the domain of perception, high-frequency words and slow speaking rate significantly improved Alexa's recognition of words produced in real time by native speakers of American English (<i>n</i> = 21). Alexa also recognized words with low neighborhood density with greater accuracy, but only at fast speaking rates. In contrast to human listeners, Alexa showed no evidence of adaptation to the speaker over time. In the domain of production, Polly's vowel duration and formants were unaffected by the lexical characteristics of words, unlike human speakers. Overall, these findings suggest that, despite certain patterns that humans and AI tools share, AI tools lack some of the flexibility that is the hallmark of human speech perception and production.