Audio annotation
Add value to your audio data by making it usable for your AI models. Thanks to our expertise and rigorous annotation processes, we provide you with accurate datasets adapted to your requirements.
🧏 Understanding audio
Transcription, segmentation, emotion detection, phonetic annotation: we structure your audio files for the training of vocal models and NLP.
🧑 Expert annotation
Our annotators are trained in the subtleties of language, accents, contexts, for a fine annotation that is useful for your use cases.
🛡️ Guaranteed quality
Double listening, cross-checking, standardization: our QA process ensures consistent, clear, and usable audio data sets.
Annotation techniques
Audio segmentation
Divide a sound recording into distinct segments according to temporal or acoustic criteria. Each segment corresponds to a significant unit, such as a speaker, phrase, music, background noise, or silence.
Verification of the format, quality and duration of the audio files to be processed
Specification of the types of segments to be identified: speaker changes, silences, sound events, etc.
Manual or automatic detection of cut points and assignment of a label to each segment (speech, music, noise, silence...)
Careful listening and adjusting time boundaries to ensure accurate segmentation
Voice recognition — Improving the performance of ASR (Automatic Speech Recognition) systems via cleaned and well-segmented data
Media analysis — Indexing podcasts, videos and shows into chapters or thematic sequences
Acoustic monitoring — Identification of specific sound events (broken glass, sirens, alarms) in a continuous audio stream
Multilingual transcription
Convert audio content in several languages into text, respecting the linguistic structure, cultural specificities and variations of each spoken language.
Identification of the languages present in the audio, linguistic transitions and the level of complexity (code-switching, accents...)
Dividing the audio into time segments, synchronized with the interventions of the different speakers and the language changes
Writing the content word for word in the original language, respecting grammar, hesitation, and oral particularities
Proofreading by native or experienced linguists to ensure fidelity, linguistic consistency and compliance with the requested format (verbatim, cleaned...)
Automatic subtitling of international content — Films, documentaries, conferences, multilingual interviews
Multilingual AI training — Training data for speech recognition and machine translation models
Global customer service — Analysis of calls in multiple languages to improve the user experience
Speech annotation
Enrich a voice recording by adding contextual, linguistic, or acoustic information, such as words spoken, emotions, intentions, interruptions, or accents. It is essential for training and evaluating automatic speech processing systems.
Determine what to annotate: words, named entities, emotions, breaks, hesitations, tone, etc.
Cleaning, cutting, and sometimes pre-transcribing voice content to facilitate annotation work
Addition of specific labels or tags to each voice event according to the defined pattern (ex: [LAUGHS], [HESITATION], [INTERRUPTION])
Cross-checking by multiple annotators or by automatic tools to ensure data consistency and reliability
Smart voice assistants — Improving the understanding of the user's intentions and nuances
Emotional analysis — Detection of emotional states in customer calls or conversational interfaces
Linguistic and sociolinguistic studies — Analysis of speech styles, regional accents, and code-switching phenomena
Audio classification
Analyze a sound recording to automatically identify and categorize types of sounds or acoustic events (speech, music, alarm, background noise, etc.). It makes it possible to structure sound information for various AI-based applications.
Establishment of target categories (e.g. speech, applause, engine, silence, rain...) according to the objectives of the project.
Cleaning, normalizing the volume, cutting into clips or time windows for better readability.
Allocation of one or more labels per audio segment, manually or semi-automatically, according to the identified sound spectrum.
Verification of the accuracy of the labels and adjustment of the data to avoid biases linked to over- or under-represented classes.
Music industry — Recognition of genres, instruments or soundscapes for automatic indexing
Well-being & health — Analysis of sounds related to sleep, cough or breathing for assisted diagnoses
Education & interactive games — Recognition of specific sounds for adapted interactive experiences
ASR data preparation
Structure, clean, and annotate audio corpora to train automatic speech recognition systems. It ensures that models learn to transcribe speech accurately, fluently, and contextually.
Gather representative voice recordings (diversity of speakers, accents, environments) and ensure legal compliance.
Accurately transcribe spoken content, then sync text to audio via word-to-word or phoneme-to-phoneme alignment.
Elimination of errors, extraneous noises, and inconsistencies. Standardization of punctuation, abbreviations, and writing conventions.
Organization of audio files and metadata (age, gender, accent, recording conditions...) according to the formats expected by ASR models.
Training speech recognition models — Creation of corpora adapted to specific contexts (medical, legal, customer service...)
Optimizing voice assistants — Improving understanding in noisy or multilingual environments
Digital accessibility — Automatic generation of subtitles for the hearing impaired
Customized vocal corpora
Assemble a set of audio recordings designed specifically to train or assess a speech processing model. These corpora are developed according to specific criteria: language, accent, domain, sound environment, type of speakers, etc.
Identification of languages, dialects, contexts of use (reading, conversation, voice commands...), and technical specifications (format, duration, number of speakers).
Selection of varied profiles according to the criteria of the project: age, gender, geographical origin, language level, etc.
Voice capture in controlled or natural conditions, depending on the case (studio, telephone, real environments...).
Verification of audio clarity, removal of non-compliant recordings, and organization of the corpus in a format that can be used by AI teams.
Training custom voice models — Creation of datasets adapted to a rare language, a local accent or a specific field (health, finance, etc.)
ASR assessment tests — Generation of balanced test corpora to measure the performance of speech recognition models
Accessibility and inclusion — Creation of corpora representing atypical voices (speech disorders, children's voices...) for more inclusive AIs
Use cases
Our expertise covers a wide range of AI use cases, regardless of the domain or the complexity of the data. Here are a few examples:
Why choose Innovatiana ?
Our added value
Extensive technical expertise in data annotation
Specialized teams by sector of activity
Customized solutions according to your needs
Rigorous and documented quality process
State-of-the-art annotation technologies
Measurable results
Boost your model’s accuracy with quality data, for model training and custom fine-tuning
Reduced processing times
Optimizing annotation costs
Increased performance of AI systems
Demonstrable ROI on your projects
Customer engagement
Dedicated support throughout the project
Transparent and regular communication
Continuous adaptation to your needs
Personalized strategic support
Training and technical support
Compatible with
your stack
We use all the data annotation platforms of the market to adapt us to your needs and your most specific requests!
Secure data
We pay particular attention to data security and confidentiality. We assess the criticality of the data you want to entrust to us and deploy best information security practices to protect it.
No stack? No prob.
Regardless of your tools, your constraints or your starting point: our mission is to deliver a quality dataset. We choose, integrate or adapt the best annotation software solution to meet your challenges, without technological bias.
Feed your AI models with high-quality, expertly crafted training data!
