Audio Annotation Services | Transcription, Diarization, Intent

Audio annotation and transcription services

Teach Your AI to Listen

Voice interfaces, automatic speech recognition, and audio intelligence systems all depend on accurately labeled audio data. Our annotation teams transcribe speech with verbatim precision, identify and segment individual speakers, classify emotional tone and intent, and label acoustic events — across dialects, accents, and noise conditions that challenge automated systems. We combine native-speaker linguists with specialized audio tooling to deliver training data that captures the full complexity of human speech.

Verbatim and normalized transcription
Speaker diarization and turn-taking annotation
Emotion and sentiment detection in speech
Intent classification for conversational AI
Acoustic event detection and environmental sound labeling

Capabilities

Audio Annotation Methods

Specialized techniques for the unique challenges of audio and speech data.

Speech Transcription

Word-for-word and normalized transcription with timestamps, punctuation, and speaker attribution. We handle overlapping speech, background noise, accented speakers, and code-switching between languages.

Speaker Diarization

Identifying and segmenting individual speakers within multi-party conversations. Each speaker is assigned a unique ID with precise start/end timestamps, enabling models to learn who said what and when.

Emotion Detection

Classifying vocal emotion (happy, sad, angry, neutral, frustrated) based on tone, pitch, pace, and linguistic cues. Supports both categorical and dimensional (valence-arousal) emotion models for nuanced sentiment analysis.

Intent Classification

Labeling spoken utterances with user intent categories for virtual assistants, IVR systems, and customer service bots. We extract intents, slots, and entities from natural conversational speech.

Sound Event Detection

Labeling non-speech audio events — alarms, machinery sounds, vehicle noises, environmental ambience — with precise timestamps. Used in security systems, industrial monitoring, and smart home applications.

Phonetic Annotation

IPA transcription and phoneme-level alignment for pronunciation modeling, text-to-speech (TTS) training, and accent analysis. Critical for building natural-sounding speech synthesis systems across languages.

FAQ

Frequently Asked Questions

We work with all standard audio formats (WAV, MP3, FLAC, OGG, M4A) at any sample rate. Our annotators are trained to handle challenging audio conditions including background noise, low bitrate recordings, phone-quality audio, and multi-speaker environments. We adapt our workflow based on audio quality and provide confidence scores for difficult segments.

We match annotators to audio by language and dialect. For Arabic, we have annotators covering Gulf, Levantine, Egyptian, and Maghreb dialects. For English, we cover American, British, Australian, Indian, and other regional accents. This native familiarity ensures accurate transcription of colloquialisms, code-switching, and dialectal variations.

We achieve 97–99% word-level accuracy for clean speech and 95%+ for challenging audio conditions. All transcriptions go through a two-pass review process: an initial transcription followed by a quality review by a second annotator. We report word error rate (WER) metrics for every batch delivered.

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Build Voice AI That Truly Understands

Send us sample audio in any language and we'll return transcribed, labeled results within 48 hours. Experience the difference native-speaker annotation makes.

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