nyralabs/disfluency_speech_german

Nyra Disfluency Speech German

nyrahealth/disfluency_speech_german is a German speech dataset for evaluating verbatim ASR: models that should transcribe not only the intended words, but also fillers, cutoffs, repetitions, and sound events.

This dataset was recorded in-house by two Nyra researchers, Berns and Laurin, with the goal of producing natural disfluent German speech similar in spirit to the English AMAAI Lab DisfluencySpeech dataset.

Like the English release, it is formatted for verbatim-transcription benchmarking with paired:

• verbatim_transcript: what the speaker actually said
• intended_transcript: a cleaned version of what the speaker meant to say

It is used by the Nyra Verbatim Speech Benchmark, which evaluates verbatim ASR in detail and breaks errors down into fillers, sounds, cutoffs, repetitions, and intended-transcript failures.

For the exact convention definitions used by the benchmark, see:

• Nyra Verbatim Speech Benchmark
• Verbatim transcript conventions

Background

This German dataset was designed as a companion to the English verbatim benchmark data.

The English reference point is:

• Dataset: amaai-lab/DisfluencySpeech
• Paper: DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage

The German release is not part of that original dataset. Instead, it is an in-house Nyra dataset that follows the same general idea: paired verbatim and intended transcripts for detailed evaluation of disfluent speech transcription.

Dataset Structure

The dataset contains 202 utterances and about 0.95 hours of audio.

Splits:

• test: 202

Features:

• id
• audio
• duration_in_s
• split
• speaker
• language
• verbatim_transcript
• intended_transcript
• timings
• verbatim_timings

Transcription Conventions

Verbatim Transcript

The verbatim_transcript follows a small set of explicit conventions so disfluencies and non-speech events can be evaluated consistently:

• Cutoffs use *, for example w*, d*, or bru*
• Fillers are bracketed tags, primarily [UH] and [UM]
• Sound events are also bracketed tags, for example [lipsmack], [throatclearing], [laughter], or [cough]
• Repetitions are written as repeated words, not as separate tags
• Spoken words are otherwise written as they were said

Example:

Also, [UM] ich denke, dass [lipsmack] wir vielleicht [UH] nächste Woche, [UM] ich meine am Wochenende, einen Ausflug machen könnten, weil das w* w* Wetter ganz gut aussieht.

Intended Transcript

The intended_transcript is the cleaned target for intended ASR. It removes disfluent material while preserving the speaker's meaning, including fillers, sound tags, repeated restarts, and cutoff fragments.

Example:

verbatim: Also, [UM] ich denke, dass [lipsmack] wir vielleicht [UH] nächste Woche, [UM] ich meine am Wochenende, einen Ausflug machen könnten, weil das w* w* Wetter ganz gut aussieht.
intended: Also, ich denke, dass wir vielleicht am Wochenende einen Ausflug machen könnten, weil das Wetter ganz gut aussieht.

This makes the dataset suitable for evaluating both:

• verbatim transcription quality
• intended transcription quality

Tag Analysis

The counts below were computed over the full dataset from verbatim_transcript.

Summary:

• utterances: 202
• utterances with at least one bracketed tag or cutoff: 202
• total bracketed tags: 846
• total cutoff tokens: 394

Fillers

Tag	Count
[UH]	348
[UM]	266

Sound Tags

Tag	Count
[throatclearing]	57
[laughter]	57
[lipsmack]	50
[cough]	21
[sniff]	16
[breath]	13
[yawn]	11
[sigh]	5
[noise]	2

Cutoffs

Marker	Count
* cutoff tokens	394

These statistics show that the German set is deliberately dense in disfluencies: every utterance contains at least one annotated event or cutoff, fillers are very frequent, and cutoff fragments occur often enough to be a core evaluation category.

Benchmark Usage

This dataset is designed to be used with the Nyra Verbatim Speech Benchmark.

That benchmark:

• derives gold disfluency labels automatically from the verbatim and intended transcript pair
• computes transcript metrics such as vWER and iWER
• computes event metrics for fillers, sounds, cutoffs, and repetitions
• provides detailed error analysis for verbatim ASR models

Citation

If you use this dataset, please cite the benchmark repository and describe that the German recordings were collected in-house by Nyra researchers Berns and Laurin as a German companion set for verbatim-ASR evaluation.