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Harnessing the wealth of audio data through accurate multimodal annotation
CASE STUDY

Harnessing the wealth of audio data through accurate multimodal annotation

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Written by
Aïcha
+500 hours

annotated and transcribed audio files

+30

labels applied to multimodal data

100%

Correspondence between audio segments and transcription

Sommaire

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In industries such as customer support, healthcare, and behavioral analysis, audio has become one of the most strategic data sources for artificial intelligence. Every conversation, phone call, or recorded consultation contains a wealth of information that goes far beyond the literal words spoken. Intonation, rhythm, pauses, hesitations, and even interruptions carry signals about intentions, emotions, and entities that can be critical for decision-making.

For example, in customer support, the ability to detect whether a caller is frustrated or satisfied allows companies to adapt responses in real time and better route conversations. In healthcare, analyzing patient speech can provide early signals of cognitive decline, stress, or depression, supporting medical practitioners in their diagnosis. Behavioral analysis, meanwhile, relies heavily on vocal markers to understand engagement, persuasion, and authenticity in communication. In all these domains, exploiting audio data effectively requires high-quality annotated datasets that capture both the linguistic and paralinguistic layers of speech.

The Mission

Create a rich, structured dataset from raw audio files, including:

  • Fine segmentation of audios in relevant chunks with timestamps. This step is key for training models that require temporal precision, such as intent detection or dialogue act recognition. Segmentation also makes it possible to link annotations directly to specific audio moments, creating a highly navigable dataset.
  • Manual transcription of segments, with correction of speech recognition errors. Automatic speech recognition (ASR) systems were used as a starting point, but human annotators carefully reviewed and corrected the transcripts. This combination allowed the dataset to retain efficiency while guaranteeing linguistic fidelity. Special attention was given to error-prone areas such as proper nouns, acronyms, domain-specific vocabulary, and overlapping speech.
  • Annotation of more than 30 labels related to content (themes, intentions, emotions, entities, interruptions...). Innovatiana defined a comprehensive annotation schema that included more than 30 categories. These ranged from content-related themes (e.g., product questions, medical symptoms, financial concerns), to intentions (complaint, request, reassurance), to emotions (anger, satisfaction, confusion). The schema also captured structural events like interruptions, hesitations, or silence markers, enabling nuanced modeling of conversational dynamics.
  • Building relationships multimodal between the transcript and the corresponding audio portions. Each transcript was aligned with the exact audio portion it referred to, enabling models to simultaneously learn from linguistic cues (words and grammar) and acoustic cues (tone, pitch, volume). This alignment is essential for training next-generation systems capable of understanding not only “what” was said, but also “how” it was said.

Innovatiana mobilized a dedicated team, expert in audio annotation and NLP, and set up a tooled process allowing both a high level of precision and complete traceability of the annotations.

Innovatiana’s Approach

To deliver on this ambitious mission, Innovatiana mobilized a specialized team of audio annotators and NLP experts. The annotators were trained to ensure consistency across a large volume of data, while experts in natural language processing designed the labeling schema and quality control methods.

A tooled annotation pipeline was deployed to ensure traceability at every stage. Each annotation could be audited, versioned, and cross-validated, which is crucial for both compliance and reproducibility. Custom dashboards and quality metrics allowed project managers to monitor inter-annotator agreement, highlight discrepancies, and quickly resolve them.

The project emphasized a balance between precision and scalability. While high-quality manual work formed the foundation, semi-automated workflows were used where appropriate to accelerate progress without compromising quality. For instance, ASR outputs were pre-aligned with audio segments, giving annotators a head start while still requiring human verification.

The Results

The outcome was a robust, structured dataset that serves as a foundation for multiple AI applications:

  • Speech-to-text model training
    The corrected transcripts provided high-quality ground truth for improving ASR systems, particularly in specialized domains where off-the-shelf speech recognition often fails.
  • Classification and intent detection
    With labels capturing emotions, intentions, and conversational structure, the dataset enables the development of classifiers capable of understanding context, detecting urgency, and prioritizing responses.
  • Multi-modal truth base
    By linking transcripts directly to their corresponding audio segments, Innovatiana produced a multi-modal dataset that can power research in speech emotion recognition, dialogue systems, and healthcare diagnostics.
  • Operational efficiency
    Thanks to the careful design of the annotation workflow, the time required for human validation was significantly reduced. Clients benefitted from datasets that were not only accurate but also delivered faster, lowering overall project costs and enabling quicker time-to-market for AI solutions.

Aïcha

Published on

12/6/2025

Aïcha

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