| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-to-speech |
| language: |
| - en |
| tags: |
| - text-to-speech |
| - tts |
| - pronunciation |
| - healthcare |
| - pharma |
| - drug-names |
| - benchmark |
| pretty_name: DOSE v1.0 (Drug-name Oral Synthesis Evaluation) |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # DOSE v1.0: Drug-name Oral Synthesis Evaluation |
|
|
| **Can your voice model say retatrutide?** |
|
|
| DOSE is a benchmark measuring how accurately text-to-speech (TTS) systems |
| pronounce pharmaceutical drug names. It covers established brands, |
| newly-approved names, and generic/INN names, each tested inside a real |
| clinical sentence rather than spoken in isolation. |
|
|
| **Version: DOSE v1.0.** Live leaderboard and five public audio samples: |
| https://synthiolabs.com/synthio-dose-benchmark |
|
|
| This public dataset is the **held-out test set only**: drug name, name type, |
| and clinical sentence. Reference pronunciations, evaluation audio, and |
| per-system scores are not released, so the set cannot be used as an |
| answer key. Email [rohit@synthiolabs.com](mailto:rohit@synthiolabs.com?subject=DOSE%20model%20submission) |
| to submit a model for scoring. |
|
|
| ## Why this matters |
|
|
| Mispronounced drug names in voice AI (IVR systems, clinical voice agents, |
| pharmacy assistants) are a patient-safety concern analogous to LASA |
| (Look-Alike/Sound-Alike) drug name confusion — a recognized WHO/ISMP/FDA |
| error category. As voice AI is deployed further into clinical workflows, |
| TTS mispronunciation of a drug name is a new, largely unmeasured extension |
| of that same risk category. |
|
|
| ## Quickstart |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("SynthioLabs/dose-benchmark", split="train") |
| |
| row = ds[0] |
| print(row["drug"], row["name_type"], row["sentence"]) |
| ``` |
|
|
| Filter by name type: |
|
|
| ```python |
| generic_names = ds.filter(lambda r: r["name_type"] == "generic") |
| brand_names = ds.filter(lambda r: r["name_type"] == "brand") |
| ``` |
|
|
| ## Dataset |
|
|
| - **274 drug names**: 128 traditional (established) + 146 new (recently |
| approved) names. One row per name. |
| - **Brand and generic/INN names** (`name_type` column). |
| - **9 TTS systems** were evaluated on these sentences (see leaderboard). |
| Those recordings and scores are not in this download. |
| - Every system read the **exact same clinical sentence** containing the |
| drug name, with no special pronunciation hints given to any system. |
|
|
| ## How DOSE is scored |
|
|
| **Reference pronunciations.** Each drug’s reference comes from public |
| pharmaceutical sources, including labels, manufacturer materials, and |
| established medical references. It is then checked against spoken clinical |
| usage so the target reflects the accepted pronunciation — not a model’s |
| guess. The reference strings themselves are held out. |
|
|
| **Judge.** Each system’s recording is scored 0–5 against that same |
| reference by automated audio comparison. Several independent judgments are |
| combined into one result; **4 or higher counts as a pass**. Human |
| reviewers are not in the scoring loop. |
|
|
| ## Columns |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `drug` | string | Drug name (brand or generic/INN) | |
| | `name_type` | class label | `brand` or `generic` | |
| | `sentence` | string | The clinical sentence to be read aloud | |
|
|
| ## Leaderboard (overall pass rate, score >= 4) |
|
|
| | Rank | System | Model | Pass rate | |
| |------|--------|-------|-----------| |
| | 1 | Synthio | Synthio RxPronounce | 91.2% | |
| | 2 | Cartesia | sonic-3.6 (beta) | 80.3% | |
| | 3 | ElevenLabs | eleven_v3 | 79.2% | |
| | 4 | OpenAI TTS | gpt-4o-mini-tts | 77.4% | |
| | 5 | xAI | Grok TTS | 77.0% | |
| | 6 | Google (Gemini TTS) | gemini-3.1-flash-tts-preview | 74.5% | |
| | 7 | Deepgram | aura-2-thalia-en | 72.6% | |
| | 8 | Cartesia | sonic-3.5 | 69.0% | |
| | 9 | Microsoft Azure | DragonHD Neural | 63.1% | |
| |
| ## Submit your model |
| |
| Email us at [rohit@synthiolabs.com](mailto:rohit@synthiolabs.com?subject=DOSE%20model%20submission) |
| to submit a model for evaluation. |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{synthio2026dosev1, |
| title = {DOSE v1.0: Drug-name Oral Synthesis Evaluation}, |
| author = {Pari, Rohit Gangadhar and {Synthio Labs}}, |
| year = {2026}, |
| url = {https://synthiolabs.com/synthio-dose-benchmark} |
| } |
| ``` |
| |
