aidataservices.inAI data collection · India

ASR datasets · Accent Adaptation

ASR Training Data for Accent Adaptation

Transcribed speech corpora built to train and evaluate automatic speech recognition, with verbatim transcription, timestamps and per-token language tagging where code-mixing occurs. Applied to accent adaptation, the specification is driven by one thing: per-accent wer spread.

Request a dataset quoteReply within one working day
Audio waveforms being prepared as ASR training data — ASR Training Data for Accent Adaptation
Service
ASR datasets
Use case
Accent Adaptation
Primary metric
Per-accent WER spread
01

Required data profile

  • Accent-band balanced speech with substrate-language tags
  • Matched content across bands for controlled comparison
ASR datasets — Accent Adaptation · written into the SOW before recordingAudio16 kHz or 48 kHz PCM WAVTranscriptionVerbatim, including disfluencies, false starts and fillersTimestampsUtterance level by default; word level on requestTaggingNoise, overlap, unintelligible, foreign-language and code-s…NormalisationRaw and normalised text columns delivered separatelyYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
Audio16 kHz or 48 kHz PCM WAV
TranscriptionVerbatim, including disfluencies, false starts and fillers
TimestampsUtterance level by default; word level on request
TaggingNoise, overlap, unintelligible, foreign-language and code-switch tags
NormalisationRaw and normalised text columns delivered separately
SplitTrain/dev/test splits with no speaker leakage across splits
Two-speaker conversational recording session in a studio — supporting asr training data for accent adaptation
Two-speaker conversational recording session in a studio
03

Process

  • Style guide authored per language, covering numerals, loanwords, script and disfluency rules
  • Transcriber calibration round with inter-annotator agreement measurement
  • First-pass transcription
  • Second-pass native review
  • Automated consistency checks against the style guide
  • Split generation with speaker-disjoint partitions
04

Metrics this feeds

  • Per-accent WER spread
  • Regression on the original accent set
05

Failure modes to design out

  • Treating Indian English as one accent
  • No substrate tagging, so the model cannot be evaluated per band

Agreement is measured, not assumed. We report word-level agreement on a held-out sample so you can judge label quality before training on it.

06

Deliverables

  • Audio plus aligned transcripts (JSON/TSV, or your schema)
  • Style guide as delivered documentation
  • Inter-annotator agreement report
  • Speaker-disjoint train/dev/test splits

Frequently asked

Is asr datasets the right service for accent adaptation?

It covers accent-band balanced speech with substrate-language tags. Most accent adaptation programmes combine it with at least one other service; we will say so in the scope rather than selling one line item.

What languages are available?

All 14 languages in the network plus Indian English accent bands.

How is the evaluation set handled?

Collected first, from speakers disjoint from the training cohort, so improvement is measurable.

Scope asr datasets for accent adaptation

Send the metric you need to move.

Request a dataset quote