# Fizz Reader: Interactive Accessible Data Visualizations Through an NLG Interface

> **NIH NIH R43** · FIZZ STUDIO LLC · 2021 · $175,784

## Abstract

Specific Aims
There are many modern approaches to accessibility in data visualizations, from simple but inadequate
alternative data tables to excellent work with tactile graphics, haptics, and sonification. Each has its benefits
and limitations, but common underlying weaknesses to the advanced methods are lack of precision and need
for reader training. By contrast, most of the 7.7 million Americans with visual disabilities are at least moderately
skilled in using a screen reader. However, this typically limits users to serial data point access, the equivalent
of a data table, which does not provide an equivalent experience to the advantages of data visualizations. It
decreases independence and professional opportunities, increases stress, decreases quality of life, and puts
vulnerable people at risk when crucial public health information is disseminated in graphical-only form, such
as in the current COVID-19 pandemic.
The long range goal of this Phase I project is to create technologies that permit authors and developers of web
sites to effortlessly publish charts, diagrams, and infographics that are fully usable by all people, in particular
people who are blind, low-vision, or have cognitive disabilities. In this Phase I SBIR project, we will assess the
feasibility of creating interactive contextual automatic descriptions that enable the reader to construct an
accurate working mental model of the data with minimal effort and time, to perform tasks and make
decisions.
Fizz Studio has created a software package, Fizz Charts, that generates accessible keyboard-browsable
charts for use on any website. We seek to enhance this with Fizz Reader, a novel interactive interface that
uses natural language generation (NLG) to enable the user to query the chart for quick answers about
each data point, its relationship to other data points and to the chart statistics, and to high-level or detailed
trends and patterns in the data. The effect is of one person explaining the chart to another over the phone,
and providing relevant and rapid answers to help the listener understand as much of the data as they wish for
a core set of 7 common chart types: bar; line; pie; histogram; scatterplot; heatmap; and flowchart.
Aim 1: Develop effective interactive NLG model and engine module
To concisely communicate relevant details to the user, we will design a comprehensive set of tasks for all
supported chart types, and a set of NLG templates for each chart component (e.g. data point, axis, title).
We will use these NLG templates to develop a software module which composes colloquial utterances
from an internal statistical data model we build from the data extracted from the chart. Each set of options
will represent the affordances optimal for the chart type (e.g. comparisons for bar charts, changes over time for
line charts). This module will have a client-server API architecture, to make it adaptable to multiple user
interface modalities, including the screen reader inter...

## Key facts

- **NIH application ID:** 10157688
- **Project number:** 1R43GM140807-01
- **Recipient organization:** FIZZ STUDIO LLC
- **Principal Investigator:** Douglas Alan Schepers
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $175,784
- **Award type:** 1
- **Project period:** 2021-03-02 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10157688

## Citation

> US National Institutes of Health, RePORTER application 10157688, Fizz Reader: Interactive Accessible Data Visualizations Through an NLG Interface (1R43GM140807-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10157688. Licensed CC0.

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