◆ RESEARCH
Backed by science
The cognitive research and studies that inspired VOZCLA's approach to voice-first productivity.
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Abstract: Transformer Architecture Analysis
The dominant sequence transduction models are based on
complex recurrent or convolutional neural networks that
include an encoder and a decoder. The best performing
models also connect the encoder and decoder through an
attention mechanism. We propose a new simple network
architecture based solely on attention mechanisms.
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Natural Text-to-Speech for Long-Form Understanding
This paper argues that well-designed long-form TTS can reduce friction and preserve understanding, synthesizing evidence from large-scale voice evaluations and comprehension research into practical design principles.
VOZCLA Research Team
VOZCLA Research — November 2025
Speech-to-Text for Thought Capture and Flow
This paper argues that well-designed STT can meaningfully reduce writing friction and preserve flow, synthesizing evidence from comparative studies of speech vs. typing, documentation settings, and cognitive load research into practical design principles.
VOZCLA Research Team
VOZCLA Research — January 2026
Choice of Voices: A Large-Scale Evaluation of TTS Voice Quality for Long-Form Content
Evaluating 18 TTS and 3 human voices across 1,090 participants, this study reveals that natural TTS approaches human quality, voice preference varies individually, and listening preserves comprehension compared to reading.
Cambre, J., Colnago, J., Maddock, J., Tsai, J., & Kaye, J.
CHI 2020 — ACM Conference on Human Factors
Smart Commands for Clean, Organize, Summarize, Rewrite, and Translate
This paper argues that well-designed smart commands can reduce extraneous cognitive load, preserve flow, and make information easier to act on by transforming raw text into more usable forms.
VOZCLA Research Team
VOZCLA Research — February 2026
We publish ongoing research about voice-first productivity workflows.
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Start with STT, refine with Smart Commands, and review with TTS before you send.