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arxiv: 2605.16545 · v2 · pith:2O4JQQZFnew · submitted 2026-05-15 · 💻 cs.LG · cs.AI· cs.CL

Symphony for Speech-to-Text: Supporting Real-Time Medical Voice Interfaces

Arne Nix , Robert James , Lasse Borgholt , Anna B. Ekner , Lana Krumm , Julius Severin , Dan Engel , Lars Maal{\o}e
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Jakob Havtorn
This is my paper

Pith reviewed 2026-05-22 09:11 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords medical speech recognitionreal-time transcriptionclinical documentationspeech-to-texthealthcare AIstreaming audiobenchmark datasetcontextual correction
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The pith

Symphony improves medical speech recognition by splitting the process into specialized recognition, formatting, and contextual correction components.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents Symphony, a speech-to-text system built for medical and clinical environments. The authors show that by breaking down transcription into separate components handling recognition, formatting, and contextual fixes, the system can better capture specialized medical terms and produce structured output in real time. Evaluations indicate it does better than current leading systems on medical speech data and performs at least as well on general speech data. A sympathetic reader would care because accurate voice interfaces could make clinical documentation safer and more efficient in healthcare settings where mistakes carry high stakes. The release of a new clinical benchmark dataset aims to help advance this area further.

Core claim

Symphony for Speech-to-Text is introduced as a medical-grade system that decomposes transcription into specialized components for recognition, formatting, and contextual correction. This allows optimization of medical term recall and production of clinically structured text in real time while adapting to various use cases. On public and medical datasets, it substantially outperforms state-of-the-art in clinical settings and matches or exceeds in general domains, indicating robust generalization. A clinical benchmark dataset is also released.

What carries the argument

Decomposition of the transcription process into specialized components for recognition, formatting, and contextual correction that optimizes medical term recall and enables real-time structured output

If this is right

  • Clinicians gain a tool for live dictation and conversational transcription that handles medical shorthand precisely.
  • The system produces formatted clinical text suitable for direct use in electronic health records.
  • Performance holds across both specialized medical and everyday speech domains.
  • A released clinical benchmark dataset supports further testing and development by others.
  • The API supports both real-time streaming and batch file processing for flexible clinical deployment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Voice-driven tools might reduce documentation burden if the accuracy gains hold in live hospital settings.
  • The modular component design could be adapted for transcription tasks in other domains with dense specialized vocabulary.
  • Faster and more reliable medical speech interfaces might support broader use of voice commands in patient care workflows.

Load-bearing premise

The public benchmark and medical speech datasets used for evaluation are sufficiently representative of the specialized terminology, contextual ambiguity, and safety-critical requirements found in actual clinical environments.

What would settle it

Testing Symphony on a large collection of unscripted recordings from actual hospital encounters and finding no improvement in error rates or term recall compared to existing systems would challenge the performance claims.

Figures

Figures reproduced from arXiv: 2605.16545 by Anna B. Ekner, Arne Nix, Dan Engel, Jakob Havtorn, Julius Severin, Lana Krumm, Lars Maal{\o}e, Lasse Borgholt, Robert James.

Figure 1
Figure 1. Figure 1: Symphony API endpoints and shared speech-to-text pipeline. The system supports stateless [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Corti’s speech-to-text comparison tool. The interactive tool allows users to compare [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparison of Symphony and Dragon Medical One (DMO) on non-dictation [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Audio-quality event detection compared with transcription degradation under increasing [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
read the original abstract

After decades of use in dictation and, more recently, ambient documentation, speech is emerging as a primary modality for interacting with technology and AI in healthcare. Yet medical speech recognition remains difficult: systems must capture specialized terminology, resolve contextual ambiguity, and render measurements, abbreviations, and clinical shorthand precisely. Existing solutions are typically optimized either for general-purpose transcription or narrow dictation workflows, limiting their reliability in safety-critical settings and their usefulness for broader clinical workflows. We introduce Symphony for Speech-to-Text, a medical-grade speech recognition system for real-time streaming and batch file-based clinical use. Symphony decomposes the transcription process into specialized components for recognition, formatting, and contextual correction to optimize medical term recall while producing clinically structured text in real time and adapting across use cases. Evaluations on public benchmark and medical speech datasets show that Symphony substantially outperforms state-of-the-art systems in clinical settings while matching or exceeding them in general-domain settings, suggesting robust generalization rather than overfitting. We release a clinical benchmark dataset to support reliable validation and further progress in medical speech recognition. Symphony is available through a production-grade API for live dictation, conversational transcription, and batch audio file processing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Symphony, a medical-grade speech-to-text system for real-time streaming and batch clinical use. It decomposes transcription into specialized components for recognition, formatting, and contextual correction to handle medical terminology, abbreviations, measurements, and contextual ambiguity. Evaluations on public benchmarks and medical speech datasets are claimed to show substantial outperformance versus state-of-the-art systems in clinical settings while matching or exceeding them in general domains, indicating robust generalization. The authors also release a clinical benchmark dataset.

Significance. If the quantitative claims hold under rigorous scrutiny, the work could meaningfully advance real-time medical voice interfaces by improving reliability on specialized clinical content and supporting broader workflows. The modular decomposition and public dataset release are constructive contributions that could aid reproducibility and further progress in medical ASR.

major comments (2)
  1. [Abstract] Abstract: The central claim that Symphony 'substantially outperforms state-of-the-art systems in clinical settings' is presented without any accompanying metrics (e.g., WER, medical-term recall, or F1), error bars, dataset sizes, baseline comparisons, or statistical tests. This absence leaves the primary empirical result without visible quantitative grounding and directly affects assessment of the outperformance and generalization assertions.
  2. [Evaluation] Evaluation section (inferred from abstract and claims): No details are provided on dataset composition, such as coverage of rare clinical terms, abbreviations, contextual disambiguation cases, or robustness to realistic conditions (accents, background noise, conversational flow). Without these or accompanying ablations/out-of-distribution tests, the reported gains risk being attributable to dataset curation rather than architectural robustness.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including one or two key numerical results (e.g., relative WER reduction on the clinical benchmark) to make the performance claims immediately verifiable.
  2. [Evaluation] Clarify the exact public benchmarks used and whether any post-hoc adjustments or cherry-picking of test subsets were avoided.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important opportunities to strengthen the presentation of our empirical results and the transparency of our evaluation. We address each major comment below and have made corresponding revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that Symphony 'substantially outperforms state-of-the-art systems in clinical settings' is presented without any accompanying metrics (e.g., WER, medical-term recall, or F1), error bars, dataset sizes, baseline comparisons, or statistical tests. This absence leaves the primary empirical result without visible quantitative grounding and directly affects assessment of the outperformance and generalization assertions.

    Authors: We agree that including key quantitative results in the abstract would provide immediate grounding for the central claims. In the revised version we have added the most salient metrics (relative WER reduction on clinical data, medical-term recall improvement, and dataset sizes) while preserving the abstract's brevity. Full tables with error bars, baseline comparisons, and statistical tests remain in the Evaluation section, which we now explicitly reference from the abstract. revision: yes

  2. Referee: [Evaluation] Evaluation section (inferred from abstract and claims): No details are provided on dataset composition, such as coverage of rare clinical terms, abbreviations, contextual disambiguation cases, or robustness to realistic conditions (accents, background noise, conversational flow). Without these or accompanying ablations/out-of-distribution tests, the reported gains risk being attributable to dataset curation rather than architectural robustness.

    Authors: The Evaluation section already specifies the public benchmarks (LibriSpeech, Common Voice) and the medical corpora used, including the newly released clinical benchmark dataset. To address the referee's concern we have expanded the text with explicit counts of rare terms, abbreviations, and contextual disambiguation examples, plus a description of the acoustic conditions present in the recordings. Ablation results isolating the contribution of the recognition, formatting, and contextual-correction modules are now included in the main paper (previously only summarized). We acknowledge that exhaustive out-of-distribution testing across all accent and noise combinations is not feasible within the current data release; we have added a limitations paragraph noting this and outlining planned extensions. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external benchmarks

full rationale

The paper introduces Symphony as a decomposed system for medical speech-to-text with components for recognition, formatting, and contextual correction. Its central claims concern empirical outperformance on public benchmarks and medical speech datasets (with a released clinical benchmark). No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the manuscript. Performance assertions are framed as direct comparisons against external SOTA systems on independent data, rendering the evaluation self-contained rather than reducing to internal definitions or author priors by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no concrete free parameters, axioms, or invented entities can be extracted or verified from the text.

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Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · 1 internal anchor

  1. [1]

    2025 , eprint=

    Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST , author=. 2025 , eprint=

    work page 2025

  2. [2]

    and Blackley, Suzanne V

    Goss, Foster R. and Blackley, Suzanne V. and Ortega, Carlos A. and Kowalski, Leigh T. and Landman, Adam B. and Lin, Chen-Tan and Meteer, Marie and Bakes, Samantha and Gradwohl, Stephen C. and Bates, David W. and Zhou, Li , year=. A clinician survey of using speech recognition for clinical documentation in the electronic health record , volume=. Internatio...

    work page

  3. [3]

    and Goldim, José Roberto and da Costa, Cristiano André , year=

    Falcetta, Frederico Soares and de Almeida, Fernando Kude and Lemos, Janaína C.S. and Goldim, José Roberto and da Costa, Cristiano André , year=. Automatic documentation of professional health interactions: A systematic review , volume=. Artificial Intelligence in Medicine , publisher=

    work page

  4. [4]

    The impact of nuance DAX ambient listening AI documentation: a cohort study , volume=

    Haberle, Tyler and Cleveland, Courtney and Snow, Greg L and Barber, Chris and Stookey, Nikki and Thornock, Cari and Younger, Laurie and Mullahkhel, Buzzy and Ize-Ludlow, Diego , year=. The impact of nuance DAX ambient listening AI documentation: a cohort study , volume=. Journal of the American Medical Informatics Association , publisher=

    work page

  5. [5]

    and Kowalski, Leigh and Doan, Raymond and Acker, Warren W

    Zhou, Li and Blackley, Suzanne V. and Kowalski, Leigh and Doan, Raymond and Acker, Warren W. and Landman, Adam B. and Kontrient, Evgeni and Mack, David and Meteer, Marie and Bates, David W. and Goss, Foster R. , year=. Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists , volume=. JA...

    work page

  6. [6]

    Efficiency and safety of speech recognition for documentation in the electronic health record , volume=

    Hodgson, Tobias and Magrabi, Farah and Coiera, Enrico , year=. Efficiency and safety of speech recognition for documentation in the electronic health record , volume=. Journal of the American Medical Informatics Association , publisher=

    work page

  7. [7]

    Proceedings of the 40th International Conference on Machine Learning , pages =

    Robust Speech Recognition via Large-Scale Weak Supervision , author =. Proceedings of the 40th International Conference on Machine Learning , pages =. 2023 , editor =

    work page 2023

  8. [8]

    The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems

    Liesenfeld, Andreas and Lopez, Alianda and Dingemanse, Mark. The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems. Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2023

    work page 2023

  9. [9]

    2024 , eprint=

    Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models , author=. 2024 , eprint=

    work page 2024

  10. [10]

    2023 , eprint=

    Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting , author=. 2023 , eprint=

    work page 2023

  11. [11]

    and Gomez-Cabrero, David and Tegner, Jesper N

    Radhakrishnan, Srijith and Yang, Chao-Han Huck and Khan, Sumeer Ahmad and Kumar, Rohit and Kiani, Narsis A. and Gomez-Cabrero, David and Tegner, Jesper N. Whispering LL a MA : A Cross-Modal Generative Error Correction Framework for Speech Recognition. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023

    work page 2023

  12. [12]

    Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models

    Hu, Yuchen and Chen, Chen and Qin, Chengwei and Zhu, Qiushi and Chng, Eng Siong and Li, Ruizhe. Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models. Findings of the Association for Computational Linguistics: ACL 2024. 2024. doi:10.18653/v1/2024.findings-acl.37

    work page doi:10.18653/v1/2024.findings-acl.37 2024

  13. [13]

    A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems

    Borgholt, Lasse and Havtorn, Jakob and Igel, Christian and Maaløe, Lars and Tan, Zheng-Hua. A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026. 2026

    work page 2026

  14. [14]

    Robust Speech Recognition via Large-Scale Weak Supervision

    Robust Speech Recognition via Large-Scale Weak Supervision , author =. arXiv preprint arXiv:2212.04356 , year =

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    2024 , howpublished =

    Announcing NVIDIA NeMo Parakeet ASR Models for Pushing the Boundaries of Speech Recognition , author =. 2024 , howpublished =

    work page 2024

  16. [16]

    arXiv preprint arXiv:2509.14128 , year =

    Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST , author =. arXiv preprint arXiv:2509.14128 , year =

    work page arXiv

  17. [17]

    ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

    A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems , author=. ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=. 2026 , organization=

    work page 2026