Audio Annotation Services
Every layer of sound, precisely labeled
Trusted by AI teams worldwide
50M+
Utterances annotated
98.5%
QA accuracy
40+
Languages
2K+
Domain expert annotators
48h
Pilot batch turnaround
Use cases
Every audio labeling task, done with precision
01
Speech Transcription
Verbatim and clean-read transcription by native-speaker annotators — covering disfluencies, fillers, false starts, overlapping speech, and domain-specific vocabulary.
02
Speaker Diarization
Segment-level speaker identity annotation — who spoke, when, for how long — with overlap detection, cross-talk flagging, and consistent speaker ID across multi-hour recordings.
03
Phoneme & Word Alignment
Fine-grained forced-alignment and manual phoneme boundary correction — including IPA transcription, stress marking, and sub-word timing for TTS and pronunciation AI training.
04
Sound Event Detection
Temporal start/end boundary labeling for environmental sounds, audio events, and scene categories — with confidence scoring and overlapping event support for complex soundscapes.
05
Emotion & Sentiment Annotation
Utterance-level emotion classification (angry, happy, neutral, sad, fearful, disgusted, surprised) and valence/arousal continuous scoring for affective computing and call centre AI.
06
Voice Activity Detection (VAD)
Binary frame-level speech/non-speech segmentation with multi-class extension for music, noise, silence, and background scene — for ASR pipeline pre-processing and audio indexing.
07
Music Annotation
Genre classification, tempo and beat detection, chord progression labeling, instrument identification, and mood tagging for music information retrieval and streaming AI systems.
08
Language & Dialect Identification
Segment-level language detection, accent classification, and code-switching boundary marking — for multilingual ASR routing, language-adaptive models, and dialect research.
09
Audio Quality Assessment
Signal-to-noise ratio scoring, clipping detection, reverberation flags, background noise classification, and overall usability ratings — for dataset filtering and quality control pipelines.
Use cases
Audio annotation for every AI application
Automatic Speech Recognition (ASR)
Text-to-Speech (TTS) Synthesis
Prosody annotation, stress marking, phoneme boundaries, and recording quality flags for neural TTS model training — across voices, speaking styles, and emotional registers.
Call Centre & Conversational AI
Diarization, transcript, intent labels, sentiment, and call quality scoring for customer service automation — supporting contact centre AI, agent assist, and voice analytics platforms.
Multilingual & Low-Resource NLP
Native-speaker transcription and phoneme annotation across 40+ languages — including regional dialects, code-switching data, and low-resource languages underserved by existing datasets.
Music AI & MIR Systems
Genre, mood, tempo, chord, and instrument annotation for music information retrieval, recommendation engines, playlist generation, and audio fingerprinting systems.
Audio Surveillance & Safety
Sound event detection, anomaly labeling, and scene classification for smart home devices, public safety systems, industrial monitoring, and environmental sound AI.
Quality assurance
QA that matches the precision audio demands
Audio annotation has unique quality challenges — disfluency conventions, dialect knowledge, phonetic accuracy, and temporal precision. Our QA pipeline is built for all of them.
Transcription accuracy is measured against a gold-standard reference set for every annotator cohort. We report word error rate (WER), character error rate (CER), and disfluency recall separately — because each matters differently depending on your downstream task.
For subjective tasks like emotion labeling and audio quality scoring, we use inter-annotator agreement (Cohen’s kappa) on calibration samples with adjudication workflows for borderline cases. IAA scores are reported in every delivery.
Native-speaker annotators
Every language transcribed by native speakers trained on your domain's vocabulary, disfluency conventions, and accent conventions — not bilingual transcribers working in a second language.
Automated pre-screening
Audio is screened for clipping, SNR below threshold, codec artifacts, and duration anomalies before annotation begins — so annotators work on usable audio from the start.
Multi-pass QA
Every annotation passes inter-annotator agreement measurement, automated consistency validation, and senior reviewer sign-off before delivery. Rejection rate and revision log included in every report.
How it works
From audio file to production-ready annotation
A transparent four-stage pipeline with quality gates at every step — designed for audio AI teams who need consistent, repeatable delivery.
Define scope
Share your audio type, annotation task, language targets, domain vocabulary, and quality requirements. We design custom annotation guidelines, disfluency conventions, and QA rubrics with your team.
Pre-screen & prepare
Uploaded audio is pre-screened for quality (SNR, clipping, artifacts), segmented to annotation-optimal lengths, and assigned to annotators trained on your specific task and domain.
Annotate & QA
Native-speaker annotators label your audio. Every batch passes IAA measurement, automated consistency checks, and senior reviewer sign-off before it leaves our pipeline.
Deliver & iterate
Receive clean annotations in TextGrid, ELAN, WebVTT, JSON, CSV, or your custom schema — with a full QA report. Ongoing batch delivery on your schedule, same annotator pool every time.
Why Synnth
Built for teams where audio quality is mission-critical
What separates Synnth from generic transcription services and crowdsourced annotation platforms — especially for the nuanced demands of audio AI training data.
Native-speaker annotators only
Every language transcribed and labeled by native speakers who understand dialect variation, natural disfluency patterns, and domain-specific pronunciation — not bilingual workers approximating a second language.
40+ languages
Domain vocabulary matched
Medical, legal, financial, and technical audio requires annotators who recognize the terminology being spoken — not transcribers who phonetically approximate words they’ve never encountered in context.
200+ domain specialists
Temporal precision QA
Phoneme boundaries, speaker turns, and event timestamps are validated for temporal accuracy — not just label correctness. Off-by-a-few-frames boundaries compound into training data errors at scale.
Frame-accurate
Custom disfluency conventions
Disfluency handling varies by downstream task — ASR verbatim transcription needs every “um” and false start; clean-read for TTS does not. We implement your exact conventions, not a generic standard.
Enterprise-grade security
All audio encrypted at rest and in transit. GDPR compliant, HIPAA-ready for healthcare audio. NDAs on every engagement. Your sensitive recordings — calls, interviews, clinical sessions — never leave controlled environments.
Fast pilot SLAs
Pilot batches of up to 10,000 utterances in 48 hours — so you can validate annotation quality, disfluency convention adherence, and phoneme accuracy before committing to full production volume.
48h pilot delivery
Input & output formats
Delivered in the format your pipeline already expects
No conversion scripts needed. Annotations arrive clean and structured, ready for ingestion into your ASR training pipeline, TTS toolkit, or audio ML framework.
Output formats
Delivered in the format your pipeline expects
FAQ
Common questions about audio annotation
Everything you need to know before starting an audio annotation project with Synnth.
💡 Can’t find your answer here? Talk to our team — we typically respond within one business day.
What is audio annotation and why does it matter for AI?
Audio annotation is the process of labeling audio recordings with structured metadata — transcriptions, speaker identities, sound event categories, phoneme boundaries, emotion tags, or other attributes — to create training data for speech and audio AI systems. The quality, diversity, and precision of audio annotations directly determines the accuracy and robustness of ASR engines, TTS systems, speaker verification models, and sound classification networks.
What is the difference between verbatim and normalized transcription?
Verbatim transcription captures exactly what was said — including filler words (“um,” “uh”), false starts, repetitions, mispronunciations, and disfluencies. This is used for ASR training where the model needs to learn to handle natural spoken language. Normalized (or “clean-read”) transcription removes disfluencies and corrects to standard written form — more appropriate for TTS training and subtitle production. Synnth can deliver either, or both in parallel, per your project requirements.
How does speaker diarization annotation work?
Speaker diarization annotation identifies and labels “who spoke when” in a multi-speaker audio recording. Annotators segment the audio into speaker turns, assign a consistent speaker ID to each segment, mark overlap regions where multiple speakers talk simultaneously, and flag non-speech events. Synnth validates that speaker IDs remain consistent across the full recording — including re-identification after long silences or when a speaker re-enters the conversation.
What audio formats does Synnth accept for annotation?
Synnth accepts WAV, FLAC, MP3, AIFF, M4A, OGG, OPUS, WMA, and most common audio formats. For telephony datasets, 8kHz PCM is supported. Annotations are delivered in your preferred format — TextGrid (Praat), ELAN EAF, WebVTT, SRT, JSON, CSV, CTM, STM, HTK Lab, or custom schemas aligned to your training pipeline.
How does Synnth ensure annotation quality for heavily accented or dialectal speech?
For accented or dialectal audio, we match annotators to the specific accent or dialect — not just the language. For example, Indian English is annotated by Indian English native speakers, not British or American annotators. For regional dialects (e.g., Egyptian Arabic, Bavarian German), we source specialist annotators with native fluency in the specific variety. Annotators are tested on calibration samples from your target accent before beginning production work.
Can Synnth annotate audio in noisy or degraded conditions?
Yes. Synnth regularly annotates challenging audio — call centre recordings, far-field microphone captures, street noise environments, and telephony codec degradation. For heavily degraded audio, we first run an audio quality assessment to flag segments below a usability threshold, then annotate the usable segments with appropriate noise and acoustic condition metadata. Unusable segments are flagged for re-recording rather than producing low-quality annotations.
What is the turnaround time for audio annotation projects?
Pilot batches of up to 10,000 utterances are typically delivered within 48–72 hours at full QA standards. For ongoing production runs, we agree velocity targets and delivery schedules during scoping. Audio annotation throughput depends on task complexity — phoneme alignment takes longer per hour of audio than simple transcription — and we provide honest velocity estimates before commitment.
How is sensitive audio data — call recordings, clinical sessions — kept secure?
All audio is transferred through TLS-encrypted channels and stored at rest with AES-256 encryption. Annotation work is performed only within access-controlled, audited environments — annotators can access assigned audio through our secure platform but cannot download or export raw files. NDAs are signed on every engagement. For healthcare audio, we operate HIPAA-ready workflows with audit trails and can sign BAAs where required.
Get started
Start your audio annotation project today
Tell us your audio type, annotation task, language targets, and volume. Our team responds within one business day with a scoping plan and no-obligation quote.
- info@synnth.com
- Mon–Fri, 9am–6pm IST
- Response within 1 business day
- No setup fees
- No setup fees
- NDA available on request
- Free pilot for qualifying projects