Audio & Speech Data
Services
Transcription, speaker diarization, intent detection, sentiment analysis, phonetic labeling, and sound event detection — with millisecond-level timestamp precision, native-speaker annotators across 20+ languages, and measurable WER/DER quality benchmarks.
Six Core Audio & Speech Services
Each capability includes specific accuracy benchmarks, throughput rates, and quality controls. All configurable to your audio processing pipeline.
Verbatim & Clean-Read Transcription
Word-level accuracy with timestamped segments and domain vocabularyVerbatim transcription preserving disfluencies, false starts, filler words, and overlapping speech — or clean-read transcription with normalized text. Word-level timestamps, speaker attribution, and domain-specific vocabulary handling for medical, legal, and technical audio.
- Verbatim mode: disfluencies, fillers (um, uh), false starts, self-corrections preserved
- Word-level timestamps: ±50ms accuracy for word boundaries
- Domain vocabulary: custom dictionaries for medical (drug names, procedures), legal (case citations), technical (product names, acronyms)
- Clean-read mode: normalized text with grammar correction and filler removal
- Utterance-level timestamps: ±100ms for sentence/turn boundaries
- Noise annotation: [laughter], [cough], [background noise], [crosstalk] tags
Speaker Diarization & Attribution
Multi-speaker segmentation with identity tracking and overlap handlingAccurate speaker segmentation, identity labeling, and turn-taking annotation in multi-party conversations, meetings, interviews, call center recordings, and podcast/broadcast content. Support for overlapping speech detection, speaker change points, and cross-session speaker linking.
- Speaker segmentation: detect and label speaker change points with ±100ms accuracy
- Overlapping speech: detected and annotated with contributing speaker IDs
- Cross-session linking: same speaker identified across multiple recordings
- Speaker identity: consistent IDs across the full recording (Speaker_1, Speaker_2, etc.)
- Speaker attributes: gender, age range, accent/dialect, emotional state, role (agent/caller)
- Turn-taking analysis: interruptions, backchannels, and pause duration annotation
Intent Classification & Slot Filling
Utterance-level intent + entity extraction for conversational AIIntent classification and slot/entity extraction at the utterance level for training voice assistants, IVR systems, chatbots, and conversational AI. Custom intent taxonomies with hierarchical structure, slot types with value normalization, and multi-intent support for complex utterances.
- Intent classification: single-intent and multi-intent per utterance
- Slot filling: entity extraction with type, value, and normalized form
- Out-of-scope detection: utterances outside the model's domain tagged separately
- Hierarchical intents: domain → intent → sub-intent (e.g., banking → transfer → scheduled)
- Slot value normalization: dates, amounts, addresses standardized to canonical forms
- Dialogue act annotation: request, inform, confirm, deny, greet, farewell
Sentiment, Emotion & Tone Analysis
Time-aligned emotional intelligence from audio and speech contentSentiment polarity, discrete emotion classification (Ekman 6 + neutral, or custom taxonomy), arousal/valence scoring, tone analysis, and sarcasm/irony detection — all time-aligned to specific utterances or segments within the audio. Powers customer experience analytics, media monitoring, and empathetic AI.
- Sentiment: 3-point (pos/neg/neutral), 5-point, or continuous scale (-1.0 to +1.0)
- Arousal/valence: 2D emotional space scoring per utterance
- Sarcasm/irony detection: flagged with confidence score and context explanation
- Emotion: Ekman 6 (anger, disgust, fear, happiness, sadness, surprise) + neutral
- Tone classification: formal, casual, urgent, empathetic, frustrated, professional
- Temporal tracking: emotion trajectory across the full conversation
Multilingual & Accent-Aware Annotation
Native-speaker transcribers across 20+ languages with dialect expertiseTranscription, diarization, and classification across 20+ languages — each with native-speaker annotators who understand dialect variations, code-switching patterns, and cultural context. Accent-aware annotation captures speaker characteristics for accent-robust ASR training.
- 20+ languages: English, Spanish, French, German, Hindi, Mandarin, Arabic, Japanese, Korean, Portuguese, Italian, Dutch, Russian, Turkish, Vietnamese, Thai, Indonesian, Polish, Swedish, Czech + more
- Code-switching: mixed-language utterances with per-word language tagging
- Script-specific: CJK character handling, Arabic diacritics, Devanagari conjuncts
- Dialect annotation: British vs. American English, Castilian vs. Latin American Spanish, etc.
- Accent classification: speaker accent type and proficiency level
- Cultural context: culturally-appropriate sentiment and intent interpretation
Sound Event Detection & Phonetic Labeling
Environmental audio classification and IPA-level phonetic annotationNon-speech audio classification (environmental sounds, music, alerts), phonetic transcription (IPA), prosody annotation (stress, intonation, rhythm), and pronunciation assessment for TTS training, voice cloning, and accessibility applications.
- Sound event detection: 100+ environmental sound categories (sirens, doorbells, machinery, etc.)
- Prosody annotation: stress patterns, intonation contours, rhythm units
- Music annotation: genre, tempo (BPM), key, mood, instrument identification
- Phonetic transcription: International Phonetic Alphabet (IPA) at phone level
- Pronunciation assessment: correct/mispronounced/unintelligible per word
- Acoustic scene classification: indoor/outdoor, quiet/noisy, reverberant/dry
Who Uses Our Audio Services
Teams building voice-first products, analyzing customer interactions, and training speech models at scale.
Voice Assistants & Smart Speakers
Wake word detection, command recognition, multi-turn conversation, and multi-language support training data. Custom intent/entity schemas for your product's domain.
- Intent taxonomy: 200+ intents with slot types
- Multi-turn dialogues: 5–10 turns with context
- Wake word training: 50K+ positive/negative samples
Call Center Analytics
Customer interaction analysis — sentiment tracking, agent quality scoring, topic classification, call summarization, and compliance monitoring training data.
- Sentiment trajectory: per-utterance emotion tracking
- Agent scorecard: 15+ quality dimensions
- Compliance flags: regulatory keyword detection
Medical Transcription
Clinical conversation transcription with medical terminology, procedure codes, and HIPAA-compliant workflows. De-identified transcripts for clinical NLP model training.
- Medical vocabulary: 10K+ terms (ICD-10, CPT)
- De-identification: PHI removal with 99.7%+ recall
- Clinical note structure: SOAP format annotation
Media, Podcasts & Accessibility
Content transcription, speaker identification, topic segmentation, subtitle generation, and audio description for media companies and accessibility compliance.
- Subtitle generation: SRT/WebVTT with timing
- Topic segmentation: chapter markers with titles
- Audio description: visual scene narration for accessibility
Multilingual Coverage
Native-speaker annotators across 20+ languages. Each language team maintains separate gold sets, calibration processes, and quality benchmarks.
Need a language not listed? We source native-speaker annotators for most languages within 2 weeks. Dialect-specific teams available for major language variants.
Audio-Specific Quality Controls
Audio annotation demands temporal precision and acoustic sensitivity. Here's how we maintain quality across languages, environments, and speaker conditions.
Word Error Rate (WER) Tracking
WER measured against expert-transcribed gold standards per batch. Targets: ≤ 3% for clean studio audio, ≤ 5% for conversational, ≤ 8% for noisy or accented. Batches exceeding thresholds are rejected and re-transcribed.
Timestamp Validation
Automated validation of word-level and utterance-level timestamps against audio waveforms. Checks for gaps, overlaps, out-of-order timestamps, and alignment drift. Critical for subtitle generation and time-aligned analytics.
Speaker Boundary Validation
Automated checks for speaker turn boundaries — no overlapping speaker labels (unless annotated overlap), consistent speaker IDs, and no orphaned segments. Cross-referenced against diarization confidence scores.
Domain Vocabulary Accuracy
Custom dictionaries for medical, legal, financial, and technical terminology. Annotators tested on domain-specific gold sets before production. Vocabulary updates distributed within 24 hours of approval. Unknown terms flagged for dictionary expansion.
Inter-Transcriber Agreement
Double-transcription of 10% random sample per batch. Character-level and word-level agreement measured. Disagreements trigger review and recalibration. Persistent disagreement patterns identified for guideline updates.
Accent & Dialect Quality Assurance
Transcribers matched to speaker accent/dialect. Accent-specific gold sets validate regional vocabulary, pronunciation variants, and code-switching patterns. Mismatched assignments flagged and reassigned.
Supported Audio & Output Formats
Input Audio Formats
Output Formats
UTL Audio Services vs. Typical Providers
| Capability | UTL Data Engine | Typical Providers |
|---|---|---|
| WER tracking per batch with rejection thresholds | Aggregate WER only | |
| Word-level timestamps (±50ms accuracy) | Utterance-level only | |
| Speaker overlap detection and annotation | ||
| 20+ languages with native-speaker transcribers | 10–12 languages | |
| Accent/dialect matching to transcribers | Generic assignment | |
| Code-switching annotation (per-word language tags) | ||
| Sound event detection (100+ categories) | Basic noise tags | |
| IPA phonetic transcription | ||
| Emotion trajectory tracking per conversation | Overall sentiment only | |
| HIPAA-compliant medical transcription | Basic redaction |
“UTL handled 50K+ hours of multilingual transcription for our voice assistant across 8 languages. Their native-speaker teams delivered WER ≤ 2.8% on clean audio with word-level timestamps. The accent-matched transcriber assignment and domain-specific medical vocabulary support were exactly what we needed.”
Audio & Speech Questions
Need Audio & Speech Data?
Let's scope your transcription, diarization, or conversational AI data pipeline — we'll design a pilot within 48 hours.
