THE SPIRELIGHT GUIDE LIBRARY
Better data starts with better understanding. Practical guides to help you build, evaluate, and source the right data for voice AI.
Find your next guide51 practical guides By the team at Spirelight
THE KNOWLEDGE YOU NEED
Speech data explained for voice AI teams: the types, the transcripts and metadata that ship with it, where it comes from, and what training-grade means.
Read guideHow to buy AI training data: build vs buy, vetting vendors on consent and QA, license terms, red flags, and where speech specialists fit.
Read guideWhat makes ASR training data accurate: accent and dialect coverage, recording conditions, transcription quality, domain match, and held-out test sets.
Read guideHow much training data do you need for a speech model? A practical framework: fine-tuning vs from scratch, language, domain, and speaker diversity.
Read guideAudio annotation explained: transcription, timestamps, speaker labels, events, intent and emotion tags, plus how human and machine-assisted QA works.
Read guideLearn how data annotation works across text, image, audio, and video, how quality assurance is run, and when a managed labeling service fits.
Read guideWhat makes good tts training data: clean studio audio, single vs multi-speaker design, phonetic and prosodic coverage, and precise transcripts.
Read guideWhy voice assistants need conversational speech data: turn-taking, overlaps, disfluencies, and real two-speaker prosody scripted audio cannot teach.
Read guideSource multilingual speech data well: accents, dialects, low-resource languages, corpus balance, and code-switching for voice AI across markets.
Read guideCompare exclusive and non-exclusive speech data licenses, model ownership, consent, GDPR, and the clauses an AI buyer should require.
Read guideSpeech data for automotive voice AI: the four 2026 assistant stacks, the EU language gap, EV cabin acoustics, and the four data products buyers need.
Read guideA practical guide to speech data quality: judging transcription accuracy, acoustic coverage, recording integrity, consent, and vendor QA before training.
Read guideHow wake word detection works and what a wake word dataset needs for reliable keyword spotting: positives, hard negatives, far-field audio, and tradeoffs.
Read guideA speaker recognition dataset needs many speakers, repeat sessions, channel variation, anti-spoofing, and biometric consent. How to scope and license one.
Read guideHow emotional speech data is collected and labeled: acted vs natural emotion, categorical vs dimensional labels, annotator agreement, and where it pays.
Read guideLearn how automatic speech recognition converts audio to text, how modern ASR models work, how accuracy is measured, and why training data matters.
Read guideHow speaker diarization differs from speaker identification, why Whisper does not diarize on its own, and how to evaluate diarization error rate.
Read guideTime-coded transcripts explained: timestamp granularity, SRT, VTT, and JSON formats, verbatim vs clean verbatim, and what AI training actually requires.
Read guideCompare LibriSpeech, Common Voice, VoxCeleb, AudioSet, and GigaSpeech by use case, license limits, and when commercial data is safer.
Read guideRLHF explained: how reinforcement learning from human feedback trains AI models, the three training stages, the human preference data behind it, and DPO.
Read guideLearn how neural TTS clones a voice, what recording inputs matter, and which authorization, rights, privacy, and contract questions buyers should assess.
Read guideLearn how call-center AI transcribes and analyzes conversations, why telephony audio breaks generic ASR, and what training data fixes it.
Read guideCompare AI training data companies: a public-evidence comparison of five speech data providers checked in August 2026, plus a wider provider list.
Read guideLooking for an Appen alternative? A fair comparison of speech and voice data providers, Defined.ai, Shaip, TELUS, Sama, and specialists, by fit.
Read guideA buyer's guide to data annotation and labeling companies: the landscape, how to evaluate one on modality fit, QA, and consent, and where speech fits.
Read guideHow to compare audio annotation companies and platforms: the criteria that matter, what changes for regulated audio, quality control, and cost drivers.
Read guideHow voice data companies differ: Appen, Defined.ai, Shaip, TELUS, Sigma.ai, and Spirelight compared across the criteria that decide which fits a project.
Read guideLibriSpeech, LJSpeech, Common Voice, and AliMeeting licences verified: which free speech datasets clear a commercial model, and which do not.
Read guideHow much call history speech analytics needs, what intents and sentiment models label automatically, and where accurate call transcripts start.
Read guideSourcing speech data for low-resource languages: what makes a language low-resource, tonal and dialect pitfalls, and how to vet native annotators.
Read guideHow custom speech data collection projects are scoped and run: scripted vs. spontaneous recording, a brief template, speaker recruitment, and live QA.
Read guidePlan a small speech dataset from 10 to 100 hours. Compare evaluation and adaptation use cases, then confirm feasibility, rights, minimums, and price.
Read guideUnderstand speech training data cost drivers, compare written quotes like for like, and use five scope scenarios to plan an evaluation or collection.
Read guideEvery automotive speech recognition dataset, open and commercial: AISHELL-5, ICMC-ASR, AVICAR, and the EU-language gap they leave for your program.
Read guideHow in-vehicle speech data collection works: three setups compared, mic rigs, the driving-condition matrix, in-cabin consent, and delivery contents.
Read guideWhat a telephony speech dataset is: 8 kHz sampling, G.711 codecs, dual-channel calls, why wideband models degrade, and where to buy it by the hour.
Read guideEvaluate call center audio datasets by channel format, transcripts, metadata, provenance, rights, lawful basis, accent coverage, and holdout design.
Read guideHow to fine-tune Whisper for phone calls: prepare 8 kHz dual-channel data, pick hours, avoid forgetting, and evaluate on real calls.
Read guideWhisper 8kHz phone audio explained: why call transcription accuracy collapses, what narrowband loss removes, and the fixes that work, ranked.
Read guideVoice agent accent problems are a training data failure, not a config bug. The evidence, five fixes ranked honestly, and how to measure before you spend.
Read guideEU AI Act guide for speech-data buyers: roles, phased dates, Article 50 transparency, Article 10 governance, GDPR, and an evidence checklist.
Read guideUse this checklist to compare speech data collection providers on consent, recruitment, QA, turnaround, pricing, and delivery evidence.
Read guideThe on-site speech data collection playbook: demographic quotas, recruiting channels, industry rigs, session failure modes, cost drivers, and consent.
Read guideHow speech AI teams recruit participants: channels compared, dialect screening, quota fill curves, incentives, no-show buffers, and consent scope.
Read guideWhat affective computing covers beyond speech, why voice carries the commercial pull, and the data a team needs before an affective feature ships.
Read guidePitch, energy, rate, pauses, voice quality, laughter, and filled pauses: which paralinguistic features are labeled by humans and which get extracted.
Read guideThe two ways to label emotion in speech: discrete classes per turn, and valence and arousal ratings. What each one trains well, and when to collect both.
Read guideWhat a talking-head dataset must contain, how GRID, VoxCeleb2, MEAD, HDTF, and CelebV-Text compare, and why license terms decide what you can ship.
Read guideThe public visual speech recognition datasets compared: GRID, LRW, LRS2, LRS3, and AVSpeech, their license limits, and when to commission your own.
Read guideASVspoof, WaveFake, and In-the-Wild compared: sizes, spoof types, licenses, and what to specify when commissioning consented genuine and spoofed pairs.
Read guideAMI, ICSI, DipCo, AISHELL-4, AliMeeting, CHiME-6 compared: sizes, mic setups, licenses, and what to specify when commissioning meeting-style collection.
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PUT YOUR KNOWLEDGE TO WORK
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