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Enhancing Speech Technologies.

Audio Annotation

IndustryHealth
TypeWeb Application
ServicesUI/UX, Frontend & Backend Development

Scenario

A tech company specializing in natural language processing and voice recognition aims to create a cutting-edge machine learning model for both natural language generation and speaker diarization. They require a robust, diverse dataset to train their AI systems effectively.

Solution

Text-to-Speech Validation

The Audio Annotation web application enables users to contribute by reading predefined sentences in different dialects. This aids in training models to understand and generate speech nuances across various linguistic groups.
Contributions are not direct; they undergo a validation process where other users listen to the recordings, confirm if the text matches the spoken content, and:
  • Edit the text to match the audio if necessary.
  • Reject the audio if it significantly deviates from the intended text or if the audio quality is poor.
This step ensures that only high-quality data is used to train the natural language generation models, enhancing their capability to produce natural-sounding speech.

Speaker Diarization Annotation

To tackle speaker diarization, contributors analyze audio clips featuring conversations between two or more speakers. Here, they:
  • Define segments where each speaker is speaking using a timeline interface, providing precise annotations.
  • If prior annotations exist, they review these for accuracy and correct them if needed, ensuring a high level of annotation integrity.
The annotated data is then fed into the machine learning pipeline:
  • Natural Language Generation: The diverse recordings help models learn how to generate speech with natural intonations, dialect-specific expressions, and voice patterns.
  • Speaker Separation: By analyzing the annotated audio, the system learns to distinguish between different speakers, enhancing the accuracy of voice-based applications like virtual assistants or transcription tools.

Outcome

  • Enhanced Data Quality: The dual-step validation process guarantees that the datasets used for training are both accurate and diverse, leading to more robust and versatile models.

  • Efficiency: The web application streamlines the data annotation process, significantly reducing manual effort, thereby expediting model development.

  • Inclusivity in AI Development: The ability to incorporate multiple dialects and speaker profiles fosters the development of speech technologies that can operate in real-world conversational settings, transcending linguistic barriers.

Why Choose Us?
Innovation at the Core

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Tailored Solutions

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From initial consultation to deployment and beyond, we’re with you at every step of the journey.

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Klaipėda, Lithuania

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