iː ɡoʊ weɪ (2021–present) is a series of performances by JC Reus that attempt to unravel the idea of the singular, individual vocal self. Through live performance with neural real-time voice transfer models, the work stages a progressive dissolution of the singular voice into multi-voiced, polyphonic, and alien configurations. This trajectory aims to unsettle the voice as a stable marker of personal identity, treating voice instead as a dynamic boundary in which multiple subjectivities can inter-be.
The performances combine traditions of dadaist phonetic poetry, extended vocal technique, and inter-genre experimentation with technical infrastructures drawn from contemporary speech synthesis and voice conversion research; however, the most deeply explored voice transfer architecture has been RAVE (Realtime Variational AutoEncoder), after first encountering this architecture at its public release at the Neural Audio Synthesis Hackathon (NASH) in 2021. These models operate in low-latency configurations suitable for performance, enabling continuous transformation between the performer’s voice and composite models incorporating multiple human voices.
The performance arc begins with a self-trained voice transfer model, establishing a perceptual baseline of vocal identity. This model is incrementally morphed into antiphonal, choric and polyphonic relationships with the voices of others - including the sound poet Jaap Blonk, the student choir of the University of Twente, voices from the artist's social media feed, field recordings of non-human vocalisations such as Gibbons, Howler Monkeys, Parrots and Bee Hives -resulting in fluid hybridizations.
Like other data-driven AI works by Reus, the broader data ecology of dataset creation and collection is treated as embodied, situated acts of being vocal. Datasets, conceived here as both archival and agential, are interlocutors between bodies at a particular moment in time. Once integrated into generative models, these traces become capable of recombination, expansion, and unforeseen forms of recombination with a living body. Voice Data Ecologies are a generator for potential musical forms to emerge, but also a place where important musical-social relationships are made. All models used in iː ɡoʊ weɪ are trained on datasets created by the artist through direct engagement with his own body and environment, or through collaborative and meaningful relationship building with other musicians, leading to the creation and collection of recorded voice. The dataset, while functioning as a technical substrate for model training, is simultaneously an intimate biometric record - a high-resolution acoustic trace of a specific physiological state - which acquires unpredictable circulation once embedded in AI systems.
The work resonates with the philosophical and theoretical writings on the complexity of voice as simultaneously sigular and relational, alongside phenomenological and cognitive accounts of perception of the voice-body gestalt. In recent iterations, iː ɡoʊ weɪ has incorporated the recorded cries of those affected by contemporary political violence, framing these sonic fragments not as symbolic quotation but as material for embodied resonance. Here, AI-mediated voice functions as a form of empathetic attunement, in which the performer’s voice-body gestalt become a mode of witness.
By bringing together technical experimentation, theoretical inquiry, and politically attentive performance practice, iː ɡoʊ weɪ contributes to interdisciplinary debates on mediated embodiment, the ethics of vocal data, and the possibilities of collective and distributed subjectivities in the age of machine listening. It proposes that AI voice transformation, far from being merely a tool of imitation, can be mobilized as a critical and affective practice for reimagining the relational space between bodies, voices, and the technologies that connect them.
