Audio Labeling services for mlAudio Transcription

Speaker-Segmented Audio Annotation

Image

Automated transcription alone couldn’t handle the nuance of real conversations with background noise and interruptions. Our human-in-the-loop workflow ensured every detail was captured and tagged.

Industry Speech AI
Data 20 hours of audio, 2 task types (segmentation and transcription)
Image
Industry Speech AI
Data 20 hours of audio, 2 task types (segmentation and transcription)

Challenge:

The project aimed to train models capable of automatically summarizing meetings and accurately distinguishing between different speakers. Our role was to prepare annotated audio data to power an AI bot designed to process and analyze conversations.
The client requested annotation of long audio fragments for model training, requiring precision and attention to detail at every step. Our tasks included:

  • Segmenting long audio files with exact timestamps (e.g., from 00:01:23 to 00:01:45).
  • Identifying speakers (e.g., Speaker-1, Speaker-2) and tagging unintelligible speech, breaths, and overlapping voices with dedicated labels.
  • Transcribing the text following accurate segmentation.

Project challenges included:

  • Long audio recordings (ranging from 16 to 60 minutes) with multiple speakers.
  • The need to tag specific sounds separately.
  • Strict accuracy requirements: no overlapping segments and precise time boundaries for each.

Solution:

  • 01

    Preparation and workflow organization:

    The project was split into two phases:

    • Audio segmentation by speaker and sound type.
    • Transcription of the segmented fragments.

    We assembled dedicated teams for each phase: 5 annotators for segmentation and 5 for transcription, minimizing the risk of errors.

  • 02

    Training materials provided to annotators included:

    • A detailed guideline document with tag examples.
    • Video tutorials on handling complex cases and avoiding common mistakes.
    • A Q&A table with clarifications from the client.

    We also organized feedback sessions through video reviews of each annotator’s initial work.

  • 03

    Data annotation process:

    • Annotators marked audio fragments with precise timestamps and assigned the appropriate tags.
    • A separate team then transcribed the segmented audio, using special tags (e.g., [NAME] for names) to annotate entities within the text.
  • 04

    Quality control:

    • We implemented a validation system with step-by-step checks for every file.
    • Validators documented all issues in tracking tables with examples and explanations.
    • In complex cases, a helpdesk was used for quick alignment with the client.

Results:

  • We delivered precise segmentation of 20 hours of complex audio.
    Thanks to our two-step workflow and robust validation system, we achieved high-quality annotation that met the client’s requirements.

Similar Cases

  • Image

    Aerial Image Annotation for Urban Planning Tools

    We annotated 132,000+ objects in 11,000 aerial images—streamlining urban planning data with scalable workflows and tailored class logic.

    Lean more
  • Image
    Image Annotation

    Semantic Segmentation for Interior Design: A Complex Multiclass Annotation Project

    How do you segment every single object in a cluttered interior photo — 30+ classes per image? We designed a multi-step annotation pipeline to handle complexity without losing precision.

    Lean more
  • Image
    Audio Transcription

    High-Load Audio Transcription

    We completed 80 hours of high-complexity audio transcription without relying on pre-labeling — leveraging a scalable workflow designed for accuracy, consistency, and speed.

    Lean more
  • Image
    Data Collection

    Audio Dataset of Children’s Laughter and Crying

    Unidata collected 750+ unique audio samples of children’s emotional expressions — enabling emotion recognition in family-focused apps.

    Lean more
  • Image
    Audio Labeling services for ml Audio Transcription

    Banking Call Categorization

    Fast-tracked annotation of 363,000 banking calls with strict privacy — boosting NLP automation for debit, credit, and deposit queries.

    Lean more

Ready to get started?

Tell us what you need — we’ll reply within 24h with a free estimate

    What service are you looking for? *
    What service are you looking for?
    Data Labeling
    Data Collection
    Ready-made Datasets
    Human Moderation
    Medicine
    Other (please describe below)
    What's your budget range? *
    What's your budget range?
    < $1,000
    $1,000 – $5,000
    $5,000 – $10,000
    $10,000 – $50,000
    $50,000+
    Not sure yet
    Where did you hear about Unidata? *
    Where did you hear about Unidata?
    Head of Client Success
    Andrew
    Head of Client Success

    — I'll guide you through every step, from your first
    message to full project delivery

    Thank you for your
    message

    It has been successfully sent!

    We use cookies to enhance your experience, personalize content, ads, and analyze traffic. By clicking 'Accept All', you agree to our Cookie Policy.