This paper presents an Augmentative and Alternative Communication (AAC) approach for minimally verbal children with Autism Spectrum Disorder. Traditional AAC systems use fixed symbol sets and pre-defined Text-to-Speech (TTS) voices, this proposed method leverages text-to-image generation and zero-shot TTS to expand expressive capabilities. Users can create visual symbols for concepts and interests, enabling richer communication. Further, zero-shot TTS allows users to upload or record personalized voices, enabling users to have individualized output. By minimizing reliance on static symbols and voices, this approach aims to increase communicative agency, personal relevance, and social validity, areas often neglected in traditional interventions. Future research will explore long-term effects on communicative skills, user satisfaction, social engagement, and adaptability across various cultural and linguistic settings, aiming to develop more dynamic and personalized AAC solutions.

A voice's gender is considered to be dictated by one's biology and cultural situation. Without modification, this determinism results in colinearity between acoustic metrics, making disentangling a metric's contribution to gender perception difficult. To study disentanglement on natural speech, we collaborate with a gender-affirming voice teacher to collect the Disentangled Source-Filter Dataset (DSFD): 45-minutes of audio along 25 Pitch, Resonance, and Weight voice configurations, coupled with Electroglottograph (EGG) measurements. Our analysis demonstrates certain acoustic and physical metrics, namely avg. F0, ∆F, Contact Quotient (CQ), and Loudness correlate with Pitch, Resonance, and Weight. Going on to perform perceptual studies of gender, naturalness, and realness, we see that ∆F is the strongest predictor of perceived gender. Perceived naturalness and realness of a voice, however, prove to be unpredictable by these acoustic metrics.

Recent developments in generative language modeling and conversational Text-to-Speech present transformative potential for enhancing Augmentative and Alternative Communication (AAC) devices. Practical application of these technologies requires extensive research and testing. To address this, we introduce ConnecTone, a modular platform designed for rapid integration and testing of language generation and speech technology. ConnecTone implements context-sensitive generative text prediction, using conversational context from Automatic Speech Recognition inputs. The system incorporates a neural TTS that supports interpolation between reading and spontaneous conversational styles, along with adjustable prosodic features. These speech characteristics are predicted using Large Language Models, but can be adjusted by users for individual needs. We anticipate ConnecTone will enable us to rapidly evaluate and implement innovations, thereby contributing to faster benefit delivery to AAC users.