Production-grade speech data for voice AI, call analytics, and multilingual model training.
Into23 helps enterprises turn raw audio into structured training data that speech systems can trust. We cover transcription, timestamping, speaker separation, emotion and event labeling, and multilingual QA across the APAC language mix that generic audio vendors often struggle to support.
Starting from $2.50 per audio minute · Final scope depends on transcription method, speaker overlap, annotation layers, and QA depth.
Orthographic and clean-read transcription for customer calls, interviews, voice commands, and speech datasets across priority languages.
We mark who spoke when, helping clients train and evaluate diarization, call analytics, and conversational AI workflows.
Add labels for sentiment, emotion, non-speech events, and speech quality where the use case needs more than raw text.
Our teams are equipped for tonal languages, mixed-language utterances, and dialect variation common across APAC datasets.
Review, sampling, and rubric-based QA keep output quality stable across large annotation programs and multiple vendor teams.
We can support guided voice recording and speech-data collection for ASR, TTS, and model-training workflows where fresh audio is required.
We align on audio types, label set, sampling method, target languages, and acceptance criteria.
Style guides, timing rules, diarization logic, and edge-case handling are calibrated before scale begins.
Native-speaking teams complete transcription and annotation with layered QA and issue tracking.
You receive clean outputs, QA notes, and recommendations for next-cycle expansion where needed.
Into23 supports multilingual audio programs that need more than verbatim transcription, including diarization, speech labeling, prompted speech collection, and QA designed around downstream model requirements.
Into23 supports customer calls, interviews, voice commands, prompted speech recordings, and any audio requiring transcription, diarization, emotion labeling, or speech quality annotation.
Tonal languages, code-switching, and dialect variation require annotators who understand the language as it is actually spoken. Generic teams miss nuances that affect downstream model quality.
Yes. We support speaker diarization, timestamping, emotion and acoustic labeling, non-speech event marking, and prompted speech collection for ASR and TTS workflows.
Get a custom quote for your transcription & audio annotation project. Our team typically responds within 24 hours.