Audio Annotation Services for AI & Speech Recognition
Build powerful speech AI models with precise, multilingual audio annotation from Australia's trusted data labeling experts.
Why Audio Annotation Quality Matters
Your speech recognition, voice AI, and audio analysis models depend on accurately labeled training data. Poor transcription, missed timestamps, and inconsistent speaker labels create models that misunderstand users and fail in production. AI Taggers delivers enterprise-grade audio annotation that ensures your speech systems understand accents, context, and acoustic nuance.
Trusted by voice AI companies, conversational AI teams, and speech research labs to annotate thousands of hours of audio across 120+ languages.
Our Audio Annotation Capabilities
Speech-to-Text Transcription
Accurate verbatim or clean-read transcription of spoken audio for training ASR (Automatic Speech Recognition) models. We handle multiple speakers, overlapping speech, background noise, accents, and technical vocabulary with expert human transcribers.
Speaker Diarization & Identification
Label who speaks when in multi-speaker audio. Segment audio by speaker turns, identify individual speakers, and maintain speaker consistency across long recordings. Essential for meeting transcription, podcast analysis, and call center AI.
Audio Classification & Tagging
Categorize audio clips by content type, acoustic environment, emotion, intent, or custom categories. Perfect for sound event detection, acoustic scene classification, and audio search systems.
Phonetic Annotation
Mark phonemes, pronunciation variants, and phonetic features for linguistic research, accent training, and advanced speech synthesis. IPA (International Phonetic Alphabet) notation available.
Emotion & Sentiment Detection
Label emotional tone, speaker sentiment, and affective states in speech for emotion AI, mental health applications, and customer experience analytics. Capture nuanced emotions beyond simple positive/negative classifications.
Audio Event Detection & Timestamping
Identify and timestamp specific sounds, events, keywords, or acoustic features within audio recordings. Used for sound effect libraries, environmental monitoring, and audio indexing systems.
Language Identification
Detect and label languages spoken in multilingual audio, including code-switching and mixed-language conversations across 120+ languages.
Intent & Dialogue Act Annotation
Label speaker intent, dialogue acts, and conversational functions for training chatbots, voice assistants, and conversational AI systems. Includes slot filling and semantic annotation.
Audio Quality Assessment
Evaluate recording quality, identify noise types, assess intelligibility, and flag technical issues for audio dataset cleaning and quality control.
Music & Sound Annotation
Label instruments, genres, tempo, key signatures, and musical events for music information retrieval, recommendation systems, and audio production tools.
120+ Multilingual Transcription Capabilities
Native-speaker audio annotation across 120+ languages with deep understanding of accents, dialects, and regional variations.
Major Languages
English (US, UK, AUS, IND, etc.), Spanish, French, German, Italian, Portuguese, Russian, Japanese, Korean, Chinese (Mandarin, Cantonese)
Middle Eastern
Arabic (Modern Standard & Dialects), Hebrew, Persian, Turkish, Urdu
South Asian
Hindi, Bengali, Tamil, Telugu, Punjabi, Marathi, Gujarati, Malayalam
Southeast Asian
Vietnamese, Thai, Tagalog, Indonesian, Malay, Burmese, Khmer
African
Swahili, Amharic, Yoruba, Zulu, Hausa, and more
European
Dutch, Swedish, Norwegian, Danish, Finnish, Polish, Czech, Greek, Romanian
Every audio project includes native speakers who understand accents, colloquialisms, cultural references, and pronunciation patterns.
Australian-Led Quality Standards
AI Taggers maintains enterprise-grade accuracy through rigorous human-in-the-loop workflows.
Multi-stage verification process
Transcriber → Senior reviewer → Quality auditor pipeline ensures every annotation meets your specifications.
100% human-verified annotations
No automated transcription shortcuts. Real linguists and domain experts validate every label for accuracy.
Inter-transcriber agreement tracking
We measure Word Error Rate (WER), consistency scores, and annotation reliability across our transcription teams.
Accent & dialect expertise
Native speakers trained to handle regional accents, non-native speech, code-switching, and pronunciation variations.
Timestamping precision
Accurate timestamps down to the millisecond for training alignment-sensitive models and creating time-synced datasets.
Edge case handling
Our QA teams flag unclear audio, overlapping speech, background noise interference, and ambiguous utterances for resolution.
Scalability for Speech AI Projects
Start with 5-10 hours of audio to validate our process, then scale to massive speech datasets without quality degradation.
Audio minutes transcribed
Languages supported
Global transcription teams
Industries We Serve
Conversational AI & Voice Assistants
Training data for Alexa-style assistants, smart home devices, IVR systems, and voice-controlled applications with diverse accents and speaking styles.
Call Center & Customer Service
Call transcription, sentiment analysis, agent quality monitoring, compliance review, and customer interaction analytics across multiple languages.
Healthcare & Medical
Medical dictation transcription, clinical note audio, doctor-patient conversations, telemedicine recordings, and pharmaceutical trial audio data.
Legal & Compliance
Court recording transcription, deposition audio, legal discovery, witness interviews, and regulatory compliance documentation.
Media & Entertainment
Podcast transcription, video captioning, subtitle creation, content indexing, and media accessibility compliance (ADA, WCAG).
Market Research
Focus group transcription, interview annotation, consumer feedback analysis, and qualitative research audio processing.
Education & E-Learning
Lecture transcription, language learning audio datasets, pronunciation assessment, and educational content accessibility.
Automotive & IoT
In-car voice command training, smart device wake word datasets, and ambient noise testing for automotive speech systems.
Why Speech AI Teams Choose AI Taggers
Linguistic expertise
Native speakers and trained linguists who understand pronunciation patterns, phonetic variation, and language-specific acoustic features.
Transcription guideline development
We collaborate with your team to create clear guidelines for verbatim vs. clean-read transcription, handling of disfluencies, and annotation edge cases.
Transparent quality metrics
Regular reporting on Word Error Rate (WER), timestamp accuracy, speaker identification precision, and transcription velocity throughout your project.
Secure & compliant workflows
Australian data oversight, NDAs, and secure transcription platforms for sensitive audio data including healthcare, legal, and proprietary content.
Format flexibility
Deliver in JSON, SRT, VTT, TXT, CSV, Praat TextGrid, Audacity labels, or your custom format requirements.
Audio Annotation Formats & Standards
Transcription Styles
- Verbatim (includes filler words, false starts, repetitions)
- Clean read (edited for readability)
- Intelligent verbatim (removes excessive fillers while maintaining meaning)
Timestamp Granularity
- Utterance-level timestamps
- Word-level timestamps
- Phoneme-level timestamps
- Custom timestamp intervals
Speaker Labels
- Speaker 1, Speaker 2 notation
- Named speaker identification
- Speaker demographic tags (age, gender, accent)
- Overlapping speech notation
Annotation Elements
- Non-speech sounds [laughter], [cough], [music]
- Unclear audio markers [inaudible], [crosstalk]
- Confidence scores for uncertain transcriptions
- Acoustic environment tags
Our Audio Annotation Process
Consultation & Guidelines Development
We review your audio data, ASR objectives, and annotation requirements. Our team develops comprehensive transcription guidelines with examples of edge cases and formatting standards.
Pilot Batch Transcription
Transcribe 5-10 hours of representative audio as a quality test. You review accuracy, we measure WER and consistency, and we refine guidelines together.
Full-Scale Production
Distributed transcription teams begin annotation with real-time QA monitoring. Weekly quality reports track accuracy, throughput, and consistency metrics.
Delivery & Continuous Improvement
Receive annotations in your preferred format with timestamps, speaker labels, and confidence scores. We incorporate feedback and improve as your model requirements evolve.
Audio Annotation Pricing Models
Per-audio-minute pricing
Standard pricing for clear audio with single speakers and minimal background noise.
Complex audio premium
Additional rates for multi-speaker conversations, heavy accents, technical vocabulary, or poor audio quality.
Timestamping options
Utterance-level (standard), word-level (premium), or phoneme-level (custom) pricing.
Volume discounts
Reduced rates for projects exceeding 100 hours with consistent audio characteristics.
Technical Specifications We Support
Real Results From AI Teams
"AI Taggers delivered the most accurate multilingual transcription we've tested—especially for handling code-switching and non-native accents."
Speech AI Lead
Voice Assistant Startup
"Their speaker diarization accuracy in noisy call center recordings far exceeded our previous vendor."
ML Engineer
Customer Analytics Platform
Get Started With Expert Audio Annotation
Whether you're building speech recognition systems, training voice assistants, or analyzing conversational data, AI Taggers delivers the audio annotation quality your speech AI needs.
Questions about audio annotation?
What type of audio transcription do you need?
How many hours of audio require annotation?
What languages are in your audio dataset?
Do you need word-level timestamps or speaker diarization?
Our team responds within 24 hours with a tailored solution for your speech AI project.