# A Tutorial on Clinical Speech AI Development: From Data Collection to Model Validation

arXiv: https://arxiv.org/abs/2410.21640 · HTML: https://mlsanity.com/paper/2410.21640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 265 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Normalized Loss Functions for Deep Learning with Noisy Labels](https://mlsanity.com/paper/2006.13554/) (2020)
- [Representation Degeneration Problem in Training Natural Language Generation Models](https://mlsanity.com/paper/1907.12009/) (2019)
- [Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations](https://mlsanity.com/paper/2303.18027/) (2023)
- [Regularization via Structural Label Smoothing](https://mlsanity.com/paper/2001.01900/) (2020)
- [Adversarial Attacks and Defenses for Speech Recognition Systems](https://mlsanity.com/paper/2103.17122/) (2021)
- [MLDemon: Deployment Monitoring for Machine Learning Systems](https://mlsanity.com/paper/2104.13621/) (2021)
- [Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today](https://mlsanity.com/paper/2306.01499/) (2023)
- [Robustness against the channel effect in pathological voice detection](https://mlsanity.com/paper/1811.10376/) (2018)
- [Introducing Model Inversion Attacks on Automatic Speaker Recognition](https://mlsanity.com/paper/2301.03206/) (2023)
- [Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer](https://mlsanity.com/paper/2310.17049/) (2023)
- [Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy](https://mlsanity.com/paper/2210.05673/) (2022)
- [Deploying clinical machine learning? Consider the following...](https://mlsanity.com/paper/2109.06919/) (2021)

## Built on by (most cited first, 1 of 1)

- [Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care](https://mlsanity.com/paper/2605.24678/) (2026)

## Abstract (arXiv)

There has been a surge of interest in leveraging speech as a marker of health for a wide spectrum of conditions. The underlying premise is that any neurological, mental, or physical deficits that impact speech production can be objectively assessed via automated analysis of speech. Recent advances in speech-based Artificial Intelligence (AI) models for diagnosing and tracking mental health, cognitive, and motor disorders often use supervised learning, similar to mainstream speech technologies like recognition and verification. However, clinical speech AI has distinct challenges, including the need for specific elicitation tasks, small available datasets, diverse speech representations, and uncertain diagnostic labels. As a result, application of the standard supervised learning paradigm may lead to models that perform well in controlled settings but fail to generalize in real-world clinical deployments. With translation into real-world clinical scenarios in mind, this tutorial paper provides an overview of the key components required for robust development of clinical speech AI. Specifically, this paper will cover the design of speech elicitation tasks and protocols most appropriate for different clinical conditions, collection of data and verification of hardware, development and validation of speech representations designed to measure clinical constructs of interest, development of reliable and robust clinical prediction models, and ethical and participant considerations for clinical speech AI. The goal is to provide comprehensive guidance on building models whose inputs and outputs link to the more interpretable and clinically meaningful aspects of speech, that can be interrogated and clinically validated on clinical datasets, and that adhere to ethical, privacy, and security considerations by design.