From audio to meaning: optimizing the performance of voice assistants through annotation

The rise of voice assistants and natural language interfaces requires perfectly structured audio databases to train oral recognition and comprehension models.

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The mission

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Set up a Workflow multimodal annotation to combine audio files and rich text transcripts.

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To meet this objective, Innovatiana has developed a comprehensive process that includes:

• The fine segmentation of audio tracks into units of meaning (sentences, keywords);
• Manual correction of transcripts and annotation of specific elements (intentions, emotions, hesitations).

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The results

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• An aligned audio-text corpus, ready for training speech recognition (ASR) and language comprehension (NLU) models;
• A better ability for voice assistants to understand the nuances of human conversations;
• A reduction in the error rate in user-AI interactions.

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👉 To find out more : Learn how audio-text annotation refines the intelligence of voice assistants.