Mainstream text-to-speech (TTS) technologies predominantly rely on binary, cisgender speech, failing to adequately represent the diversity of gender expansive (e.g., transgender and/or nonbinary) people. This poses challenges, particularly for users of Speech Generating Devices (SGDs) seeking TTS voices that authentically reflect their identity and desired expressive nuances. This paper introduces a novel approach for constructing a palette of controllable gender-expansive TTS voices using recordings from 14 gender-expansive speakers. We employ Constrained PCA to extract gender-independent speaker identity vectors from x-vectors, using acoustic Vocal Tract Length (aVTL) as a known component.The result is applied as a speaker embedding in neural TTS, allowing control over the aVTL and several emergent properties captured as a representation of the vocal space across speakers. In addition to quantitative metrics, we present a community evaluation conducted by nonbinary SGD users.