Audio Annotation & Speech Labeling Services for Voice AI

Production-ready audio datasets in 150+ languages — speech labeling, transcription, speaker diarization and acoustic event tagging, delivered by specialist annotators

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Audio annotation

What is Audio Annotation?

Audio annotation is the process of labelling spoken words, sounds, speakers, emotions and acoustic events in an audio file so that machine learning models — automatic speech recognition (ASR), voice assistants, conversational AI and generative voice AI — can interpret real-world sound. Shaip delivers audio annotation as a managed service across 150+ languages, combining trained linguist annotators with AI-assisted tooling and a 6-Sigma quality framework.

Our Expertise

Custom Audio Labeling / Annotation isn’t a distant dream anymore

Speech & Audio labeling services have been a forte of Shaip since the beginning. Develop, train & improve conversational AI, chatbots, and speech recognition engines with our state-of-the-art audio & speech labeling solutions. Our network of qualified linguists across the globe with an experienced project management team can collect hours of multilingual audio and annotate large volumes of data to train voice-enabled applications. We also transcribe audio files to extract meaningful insights available in audio formats. Now choose the audio & speech labeling technique that best suits your goal and leave brainstorming and technicalities to Shaip.

Audio transcription

Speech Transcription & Timestamping

Verbatim, non-verbatim and phonetic transcription with speaker IDs and word-level timestamps, ready for ASR and STT model training. Output in JSON, TextGrid, ELAN, CTM and custom schemas, for production-grade datasets.

Speech labeling

Speech Labeling

Speech or Audio labeling is a standard annotation technique that concerns separating sounds and labeling with specific metadata. The essence of this technique involves ontological identification of sounds from a piece of audio and accurately annotating them to make the training datasets more inclusive

Audio classification

Acoustic Event & Sound Classification

Labels non-speech audio — alarms, coughs, gunshots, machine sounds, traffic, footsteps — for environmental sound recognition, surveillance, predictive maintenance and clinical respiratory AI. Single-label or multi-label, with custom taxonomies aligned to client schemas and AudioSet-compatible exports.

Multilingual audio data services

Multilingual Audio Annotation

Native-speaker annotators across 150+ languages and dialects — including low-resource and Indic languages — handling code-switched recordings, regional accents and culturally specific terminology. Useful where global voice AI deployments need linguistic coverage that English-only or single-locale vendors can't sustain.

Natural language utterance

Natural Language Utterance (NLU) & Intent Annotation

Intent, entity and slot tagging on spoken language, with dialect, semantic and sentiment layers. The dataset format powers chatbots, IVR systems, voice assistants and generative voice agents trained to handle real conversation, including code-switching across two or more languages within a single utterance.

Multi-label annotation

Multi-Label
Annotation

Annotating audio data by resorting to multiple labels is important to help models differentiate overlapping audio sources. In this approach, an audio dataset might belong to one or many classes, which need to the explicitly conveyed to the model for better decision making.

Speaker diarization

Speaker Diarization & Identification

Boundary detection that splits long-form recordings — call centre conversations, clinical consults, meetings — into homogenous segments per speaker. Includes gender, age-band and language tagging where the use case requires, helping models attribute speech accurately in multi-speaker environments.

Phonetics transcription

Phonetic Transcription

Unlike regular transcription that converts audio into a sequence of words, a phonetic transcription notes how words are pronounced and visually represents the sounds using phonetic symbols. Phonetic transcription makes it easier to note the difference in pronunciation of the same language in several dialects.

Audio Annotation for Generative & Multimodal AI

Specialist labelling for generative voice AI, RLHF for audio outputs, multimodal training data combining speech with text or video, and TTS dataset preparation. Includes prompt-response audio pairs, preference ranking and style/tone labels for fine-tuning conversational and voice-cloning models.

Types of Audio Classification

Acoustics Data Classification

Sounds are classified by recording environment — schools, homes, cafes, public transport, vehicles — to train speech recognition, virtual assistants, audio libraries and surveillance systems that need to recognise context, not just words.

Non-music, non-speech sound events — horns, sirens, gunshots, glass breaking, children playing, machinery — are labelled for security AI, predictive maintenance and smart-city deployments where pattern-based classification doesn’t apply.

 Genre, instrument, mood, tempo and ensemble labels for music libraries, recommendation systems, copyright detection and content moderation. Includes multi-label tagging for tracks that span genres or moods.

Intent and meaning are extracted at the utterance level — dialect, semantics, stress, tone — to power chatbots, voice assistants and conversational AI that respond to how something is said, not just what is said.

Speech & Audio Annotation Tool Powered by Human Intelligence

Despite collecting data at length, machine learning models aren’t expected to understand context and relevance, on their own. Even if self-learning NLP models were there to be deployed, the initial phase of training or rather supervised learning would require them to be fed with metadata-layered audio resources.

This is where Shaip comes into play by making state-of-art datasets available to train AI and ML setups, as per the standard use cases. Our professional workforce and a team of expert annotators are always on the job to label and categorize speech data in relevant repositories.

Speech annotation
  • Enrich natural language processing setups with granular audio data
  • Experience In-person and remote annotation facilities
  • Explore the best noise-eliminating techniques like multi-label annotation, hands-on

Reasons to choose Shaip as your Trustworthy Audio Annotation Partner

People

People

Dedicated and trained teams:

  • 30,000+ collaborators for Data Creation, Labeling & QA
  • Credentialed Project Management Team
  • Experienced Product Development Team
  • Talent Pool Sourcing & Onboarding Team

Process

Process

Highest process efficiency is assured with:

  • Robust 6 Sigma Stage-Gate Process
  • A dedicated team of 6 Sigma black belts – Key process owners & Quality compliance
  • Continuous Improvement & Feedback Loop

Platform

Platform

The patented platform offers benefits:

  • Web-based end-to-end platform
  • Impeccable Quality
  • Faster TAT
  • Seamless Delivery

Why you should outsource Audio Data Labeling / Annotation

Dedicate Team

It is estimated that data scientists spend over 80% of their time in data cleaning and data preparation. With outsourcing, your team of data scientists can focus on continuing the development of robust algorithms leaving the tedious part of the job, to us.

Better Quality

Dedicated domain experts, who annotate day-in and day-out will – any day – do a superior job when compared to a team, that needs to accommodate annotation tasks in their busy schedules. Needless to say, it results in better output.

Scalability​

Even an average Machine Learning (ML) model would require labeling large chunks of data, which requires companies to pull in resources from other teams. With data annotation consultants like us, we offer domain experts who dedicatedly work on your projects and can easily scale operations as your business grows.

Eliminate Internal Bias

The reason why AI models fail, is because teams working on data collection and annotation unintentionally introduce bias, skewing the end result and affecting accuracy. However, the data annotation vendor does a better job at annotating the data for improved accuracy by eliminating assumptions and bias.

Services Offered

Expert image data collection isn’t all-hands-on-deck for comprehensive AI setups. At Shaip, you can even consider the following services to make models way more widespread than usual:

Text annotation

Text Annotation Services

We specialize in making textual data training ready by annotating exhaustive datasets, using entity annotation, text classification, sentiment annotation, and other relevant tools.

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Image annotation

Image Annotation Services

We take pride in labeling, segmented image datasets to train discerning computer vision models. Some of the relevant techniques include boundary recognition & image classification.

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Video annotation

Video Annotation Services

Shaip offers high-end video labeling services for training Computer Vision models.
The aim here is to make datasets usable with tools like pattern recognition,object detection, and more.

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Get Audio Annotation Experts On-board.

Now prepare well-researched, granular, segmented, and multi-labeled audio datasets for intelligent AIs

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Audio annotation is the process of labelling spoken words, sounds, speakers, emotions and acoustic events in an audio file so machine learning models can interpret real-world sound. Transcription only converts speech into text — annotation goes further by tagging who is speaking, what language they’re using, what emotions or background sounds are present, and where in the audio each event occurs. Voice assistants, ASR systems and conversational AI all need annotated, not just transcribed, audio.
Shaip provides speech transcription with timestamping, speaker diarization and identification, acoustic event and sound classification, natural language utterance (NLU) and intent annotation, phonetic transcription, multi-label annotation for overlapping audio sources, multilingual audio annotation in 150+ languages, and specialist labelling for generative voice AI including RLHF preference ranking and TTS dataset preparation. Annotation is delivered as a managed service with optional dedicated teams.
 
Shaip supports audio annotation for healthcare and clinical voice AI (including respiratory event detection and physician dictation), conversational AI and voice assistants, ASR/STT for multilingual and noisy environments, call-centre analytics, automotive in-cabin voice, and generative voice AI including TTS and voice cloning. Each vertical is supported by domain-experienced annotators and, where required, named-framework compliance such as HIPAA for clinical workloads.
 
Audio annotation at Shaip runs under a 6-Sigma stage-gate quality framework with multi-tier review: annotator self-check, peer review, expert audit and statistical sampling. Inter-annotator agreement is measured and typically held at 95%+ depending on task complexity. Native-speaker annotators are used for every language, AI-assisted pre-annotation reduces variance, and a dedicated team of 6-Sigma black belts owns process compliance and continuous improvement loops.
 
Shaip’s annotator network covers 150+ languages and dialects, including all major European, East Asian and Middle Eastern languages, Indic languages, African languages and several low-resource languages. Code-switched recordings — where two languages alternate within a single utterance — are handled by multilingual annotators, which is critical for global voice AI deployments serving bilingual or multilingual users.
 
Yes. Audio annotation workflows run under an ISO 27001-certified information-security management system, are HIPAA-ready for protected health information including PHI redaction, and are GDPR-compliant for EU-resident data subjects. Access controls and audit logs are SOC 2-aligned, and NDA-bound dedicated annotator teams or on-premises annotation can be arranged for the most sensitive datasets.
Generative voice AI and large voice models need data beyond standard transcription. Shaip provides prompt-response audio pairs, RLHF preference ranking on voice outputs, multi-speaker labelled corpora for voice cloning, voice-style and emotion tagging, and TTS dataset preparation. Output is delivered in formats compatible with common fine-tuning pipelines, with linguistic and cultural diversity controlled across speakers to reduce model bias.
 
Yes. Shaip’s annotation pipeline accommodates background-noise overlays, code-switching, field-recording conditions and domain-specific terminology — medical, legal, financial, automotive and industrial. Acoustic event taxonomies can be tailored to the client’s use case, from clinical respiratory events (coughs, wheezes) to industrial sounds (alarms, machinery) to security-relevant events (gunshots, glass breaking), with custom or AudioSet-compatible exports.
 

It provides labeled data to help systems identify words, accents, and intent, improving transcription and understanding.

Challenges include handling accents and dialects. Shaip manages this with global linguists and scalable processes.