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This study examines apparent-time variation in the use of multiple acoustic cues present on coarticulatorily nasalized vowels in California English. Eighty-nine listeners ranging in age from 18-58 (grouped into 3 apparent-time categories based on year of birth) performed lexical identifications on syllables excised from words with oral and nasal codas from six speakers who produced either minimal (n=3) or extensive (n=3) anticipatory nasal coarticulation (realized by greater vowel nasalization, F1 bandwidth, and diphthongization on vowels in CVN contexts). Results showed no differences across listeners’ identification for Extensively coarticulated vowels, as well as oral vowels by both types of speakers (all at-ceiling). Yet, performance for the Minimal Coarticulators’ nasalized vowels was lowest for the older listener group and increased over apparent-time. Perceptual cue-weighting analyses revealed that older listeners rely more on F1 bandwidth, while younger listeners rely more on acoustic nasality, as coarticulatory cues providing information about lexical identity. Thus, there is evidence for variation in apparent- time in the use of the different coarticulatory cues present on vowels. Younger listeners’ cue weighting allows them flexibility to identify lexical items given a range of coarticulatory variation across (here, younger) speakers, while older listeners’ cue weighting leads to reduced performance for talkers producing innovative phonetic forms. This study contributes to our understanding of the relationship between multidimensional acoustic features resulting from coarticulation and the perceptual re-weighting of cues that can lead to sound change over time. 

This study investigates how California English speakers adjust nasal coarticulation and hyperarticulation on vowels across three speech styles: speaking slowly and clearly (imagining a hard-of-hearing addressee), casually (imagining a friend/family member addressee), and speaking quickly and clearly (imagining being an auctioneer). Results show covariation in speaking rate and vowel hyperarticulation across the styles. Additionally, results reveal that speakers produce more extensive anticipatory nasal coarticulation in the slow-clear speech style, in addition to a slower speech rate. These findings are interpreted in terms of accounts of coarticulation in which speakers selectively tune their production of nasal coarticulation based on the speaking style. 

Two studies investigated the influence of conversational role on phonetic imitation toward human and voice-AI interlocutors. In a Word List Task, the giver instructed the receiver on which of two lists to place a word; this dialogue task is similar to simple spoken interactions users have with voice-AI systems. In a Map Task, participants completed a fill-in-the-blank worksheet with the interlocutors, a more complex interactive task. Participants completed the task twice with both interlocutors, once as giver-of-information and once as receiver-of-information. Phonetic alignment was assessed through similarity rating, analysed using mixed effects logistic regressions. In the Word List Task, participants aligned to a greater extent toward the human interlocutor only. In the Map Task, participants as giver only aligned more toward the human interlocutor. Results indicate that phonetic alignment is mediated by the type of interlocutor and that the influence of conversational role varies across tasks and interlocutors. 

Increasingly, people are having conversational interactions with voice-AI systems, such as Amazon’s Alexa. Do the same social and functional pressures that mediate alignment toward human interlocutors also predict align patterns toward voice-AI? We designed an interactive dialogue task to investigate this question. Each trial consisted of scripted, interactive turns between a participant and a model talker (pre-recorded from either a natural production or voice-AI): First, participants produced target words in a carrier phrase. Then, a model talker responded with an utterance containing the target word. The interlocutor responses varied by 1) communicative affect (social) and 2) correctness (functional). Finally, participants repeated the carrier phrase. Degree of phonetic alignment was assessed acoustically between the target word in the model’s response and participants’ response. Results indicate that social and functional factors distinctly mediate alignment toward AI and humans. Findings are discussed with reference to theories of alignment and human-computer interaction. 

This study tests speech-in-noise perception and social ratings of speech produced by different text-to-speech (TTS) synthesis methods. We used identical speaker training datasets for a set of 4 voices (using AWS Polly TTS), generated using neural and concatenative TTS. In Experiment 1, listeners identified target words in semantically predictable and unpredictable sentences in concatenative and neural TTS at two noise levels (-3 dB, -6 dB SNR). Correct word identification was lower for neural TTS than for concatenative TTS, in the lower SNR, and for semantically unpredictable sentences. In Experiment 2, listeners rated the voices on 4 social attributes. Neural TTS was rated as more human-like, natural, likeable, and familiar than concatenative TTS. Furthermore, how natural listeners rated the neural TTS voice was positively related to their speech-in-noise accuracy. Together, these findings show that the TTS method influences both intelligibility and social judgments of speech — and that these patterns are linked. Overall, this work contributes to our understanding of the of the nexus of speech technology and human speech perception. 

Speech alignment is where talkers subconsciously adopt the speech and language patterns of their interlocutor. Nowadays, people of all ages are speaking with voice-activated, artificially-intelligent (voice-AI) digital assistants through phones or smart speakers. This study examines participants’ age (older adults, 53–81 years old vs. younger adults, 18–39 years old) and gender (female and male) on degree of speech alignment during shadowing of (female and male) human and voice-AI (Apple’s Siri) productions. Degree of alignment was assessed holistically via a perceptual ratings AXB task by a separate group of listeners. Results reveal that older and younger adults display distinct patterns of alignment based on humanness and gender of the human model talkers: older adults displayed greater alignment toward the female human and device voices, while younger adults aligned to a greater extent toward the male human voice. Additionally, there were other gender-mediated differences observed, all of which interacted with model talker category (voice-AI vs. human) or shadower age category (OA vs. YA). Taken together, these results suggest a complex interplay of social dynamics in alignment, which can inform models of speech production both in human-human and human-device interaction.