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Balancing Speech, Language and Hearing Science with Machine Learning Modeling in the Age of AI: “Know your Problem, Data, and Solution”

John Hansen

University of Texas at Dallas

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Analyze this Interspeech 2026 paper now: "Balancing Speech, Language and Hearing Science with Machine Learning Modeling in the Age of AI: “Know your Problem, Data, and Solution”" (ID: hansen26_interspeech). Lead with a useful explanation of its mechanism and significance, not a digest restatement or an upfront clarification question. Complete the analysis below using the evidence you can access.

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Wiki: https://interspeech-2026-wiki.vercel.app/papers/hansen26_interspeech/
Raw Markdown: https://interspeech-2026-wiki.vercel.app/papers/hansen26_interspeech/markdown.md

## Paper metadata and wiki digest

```markdown
---
id: hansen26_interspeech
title: "Balancing Speech, Language and Hearing Science with Machine Learning
  Modeling in the Age of AI: “Know your Problem, Data, and Solution”"
authors:
  - John Hansen
year: 2026
isca_url: https://www.isca-archive.org/interspeech_2026/hansen26_interspeech.html
pdf_url: https://www.isca-archive.org/interspeech_2026/hansen26_interspeech.pdf
session: "Keynote1 - John Hansen: Balancing Speech, Language and Hearing Science
  with Machine Learning Modeling in the Age of AI: “Know your Problem, Data, and
  Solution”"
topics:
  - evaluation
category: resources-evaluation
institutions:
  - University of Texas at Dallas
code:
  url: ""
  license: ""
open_to_collaboration: false
wiki_frontmatter:
  id: hansen26_interspeech
  category: resources-evaluation
  institutions:
    - University of Texas at Dallas
  updated: 2026-09-28
  confidence: abstract-only
  source: https://www.isca-archive.org/interspeech_2026/hansen26_interspeech.html
wiki_url: https://interspeech-2026-wiki.vercel.app/papers/hansen26_interspeech/
markdown_url: https://interspeech-2026-wiki.vercel.app/papers/hansen26_interspeech/markdown.md
---

# Balancing Speech, Language and Hearing Science with Machine Learning Modeling in the Age of AI: "Know your Problem, Data, and Solution"

**Category:** `resources-evaluation`

**TL;DR** — A keynote-style talk argues that speech and hearing science principles need to stay central alongside machine learning advances, drawing on decades of research into speaker variability, human perception, and large-scale conversational corpora.

## Problem

As machine learning and general AI drive major performance gains in speech technology, the talk argues that the underlying speech, language, and hearing science principles are increasingly under-emphasized in system development.

## Method

The talk surveys ways to balance speech/language/hearing science with ML modeling, covering speaker and speech variability (stress, emotion, vocal effort, Lombard effect, non-nativeness), human perception (e.g. cochlear implant innovations), and large-scale conversational language research such as team communications and historical archives.

## Results

As an invited talk rather than an empirical study, its "result" is a set of historical lessons and forward-looking guidance for combining domain science with AI-era speech modeling, aimed particularly at early-career researchers.

## Code

None released (as of this page's `updated` date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md.

## Applications

Framing research agendas and training for the next generation of speech, hearing, and human-communication AI systems that stay grounded in domain science.

## Institutions / 機構

University of Texas at Dallas

## Related

- [Do speech foundation models perceive speaker similarity as humans do?](kishi26_interspeech.md) — same problem · relatedness 1.6/3
- [Bridging the Speech AI Accessibility Gap for Deaf and Hard of Hearing People](glasser26_interspeech.md) — same problem · relatedness 1.5/3
- [MSU-Bench: Towards Understanding the Conversational Multi-Speaker Scenarios](sun26j_interspeech.md) — same problem · relatedness 1.5/3

<sub>All 950k paper pairs scored by TypeSafe Jev (`scripts/related/`); relatedness 0 = unrelated … 3 = directly comparable.</sub>
```

This summary is based on the abstract only. Details beyond the abstract have not been verified against the full paper.

TL;DR — A keynote-style talk argues that speech and hearing science principles need to stay central alongside machine learning advances, drawing on decades of research into speaker variability, human perception, and large-scale conversational corpora.

Problem

As machine learning and general AI drive major performance gains in speech technology, the talk argues that the underlying speech, language, and hearing science principles are increasingly under-emphasized in system development.

Method

The talk surveys ways to balance speech/language/hearing science with ML modeling, covering speaker and speech variability (stress, emotion, vocal effort, Lombard effect, non-nativeness), human perception (e.g. cochlear implant innovations), and large-scale conversational language research such as team communications and historical archives.

Results

As an invited talk rather than an empirical study, its "result" is a set of historical lessons and forward-looking guidance for combining domain science with AI-era speech modeling, aimed particularly at early-career researchers.

Code

None released (as of this page's updated date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md.

Applications

Framing research agendas and training for the next generation of speech, hearing, and human-communication AI systems that stay grounded in domain science.

Institutions

University of Texas at Dallas

Do speech foundation models perceive speaker similarity as humans do? — same problem · relatedness 1.6/3
Bridging the Speech AI Accessibility Gap for Deaf and Hard of Hearing People — same problem · relatedness 1.5/3
MSU-Bench: Towards Understanding the Conversational Multi-Speaker Scenarios — same problem · relatedness 1.5/3

All 950k paper pairs scored by TypeSafe Jev (scripts/related/); relatedness 0 = unrelated … 3 = directly comparable.

SOURCE & COVERAGE

AI-assisted abstract summary. Check important claims against the original paper.