# A Cross Community Agenda for Speech AI

arXiv: https://arxiv.org/abs/2609.13168 · HTML: https://mlsanity.com/paper/2609.13168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 48 of its 146 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 30 of 48)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [What Will it Take to Fix Benchmarking in Natural Language Understanding?](https://mlsanity.com/paper/2104.02145/) (2021)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Categorizing Variants of Goodhart's Law](https://mlsanity.com/paper/1803.04585/) (2018)
- [Intrinsic Bias Metrics Do Not Correlate with Application Bias](https://mlsanity.com/paper/2012.15859/) (2020)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [On The Landscape of Spoken Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2504.08528/) (2025)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances](https://mlsanity.com/paper/2409.11360/) (2024)
- [Envisioning Communities: A Participatory Approach Towards AI for Social Good](https://mlsanity.com/paper/2105.01774/) (2021)
- [Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities](https://mlsanity.com/paper/2207.11345/) (2022)
- [Social Bias Benchmark for Generation: A Comparison of Generation and QA-Based Evaluations](https://mlsanity.com/paper/2503.06987/) (2025)
- [Model-Based Approach for Measuring the Fairness in ASR](https://mlsanity.com/paper/2109.09061/) (2021)
- [WildSpeech-Bench: Benchmarking End-to-End SpeechLLMs in the Wild](https://mlsanity.com/paper/2506.21875/) (2025)
- [1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis](https://mlsanity.com/paper/2406.11727/) (2024)
- [AccentBox: Towards High-Fidelity Zero-Shot Accent Generation](https://mlsanity.com/paper/2409.09098/) (2024)
- [On Goodhart's law, with an application to value alignment](https://mlsanity.com/paper/2410.09638/) (2024)
- [When Large Language Models Meet Speech: A Survey on Integration Approaches](https://mlsanity.com/paper/2502.19548/) (2025)
- ["It's not a representation of me": Examining Accent Bias and Digital Exclusion in Synthetic AI Voice Services](https://mlsanity.com/paper/2504.09346/) (2025)
- [Mind the Gap! Static and Interactive Evaluations of Large Audio Models](https://mlsanity.com/paper/2502.15919/) (2025)
- [Aligning ASR Evaluation with Human and LLM Judgments: Intelligibility Metrics Using Phonetic, Semantic, and NLI Approaches](https://mlsanity.com/paper/2506.16528/) (2025)
- [Do Bias Benchmarks Generalise? Evidence from Voice-based Evaluation of Gender Bias in SpeechLLMs](https://mlsanity.com/paper/2510.01254/) (2025)
- [An analysis of degenerating speech due to progressive dysarthria on ASR performance](https://mlsanity.com/paper/2211.00089/) (2022)
- [Designing for Harm Reduction: Communication Repair for Multicultural Users' Voice Interactions](https://mlsanity.com/paper/2403.00265/) (2024)
- [Fairness of Automatic Speech Recognition: Looking Through a Philosophical Lens](https://mlsanity.com/paper/2508.07143/) (2025)
- [Towards a Single ASR Model That Generalizes to Disordered Speech](https://mlsanity.com/paper/2412.19315/) (2024)

## Abstract (arXiv)

Speech AI, any AI system that recognizes, transforms, or generates speech, is built and evaluated across two communities with only a small overlap: technical natural language processing (NLP) venues (e.g., ACL, ICASSP, Interspeech), and sociotechnical HCI venues (e.g., ASSETS, CHI, FAccT). In this position paper, we work toward a cross-community synthesis, organizing our critique around three problems: speech AI operates with an incomplete model of communication; it operates with an incomplete model of identity; and its metrics measure the wrong constructs. We draw on AAC as a setting where these failures are most visible and their stakes highest, alongside other underserved speakers - people who stutter, multilingual speakers, and non-binary and transgender users. For each problem we offer solution sketches oriented toward designing for human variability, nearly all of which require quantitative and qualitative methods in combination. We close on the venue structures that hold these methods apart, and on what program committees and individual authors can do to bring them together.