Start from where the model will be used
The most common reason a Khmer ASR model disappoints is a mismatch between the training audio and real use. A model trained on clean, read news sentences will stumble on a customer calling a bank from a moto taxi. Before you collect a single clip, write down:
- The channel. Phone calls (8 kHz), app voice input (16 kHz), smart speakers and in-car systems (far-field) each sound different.
- The speaking style. Read prompts, short commands, or free conversation with hesitations and overlapping speech.
- The domain. Banking, health, government services, retail and media each have their own vocabulary.
- The users. Which provinces, age groups and education levels you expect, and whether diaspora speakers matter.
This short brief drives every later decision: who you recruit, what they say, where they record and how the audio is transcribed.
Plan the speaker mix
Khmer is spoken differently across Cambodia. Phnom Penh speech is often faster and drops or merges sounds; Battambang, Takeo and other provinces have their own pronunciation; Khmer Krom speakers from southern Vietnam sound different again. A dataset made only of young, urban speakers will work well for them and poorly for everyone else.
Balance by more than gender
Set targets for province (or region), age band and gender, and track them as recording progresses. Aim for many speakers with moderate amounts of audio each rather than a few speakers with hours each: models generalise from variety, not from one voice repeated.
Keep speakers apart between training and test
Assign every speaker an ID and make sure nobody in your test set also appears in training. Otherwise your test score measures how well the model memorised voices, not how well it understands Khmer.
Write scripts that reflect real Khmer
For read speech, prompts should cover the words, numbers and names your users actually say. Good Khmer scripts:
- Mix everyday vocabulary with your domain terms, product names and place names.
- Include numbers, dates, prices in riel and dollars, phone numbers and addresses, read the way people really say them.
- Include common English and French loanwords and code-switched phrases, because that is how many Cambodians talk.
- Avoid long literary sentences that nobody would say aloud.
Read speech is cheaper and easier to transcribe, but conversational speech is what makes a model robust. Most production datasets combine both: read prompts for coverage, and guided or free conversation for natural rhythm, fillers and hesitations.
Record in the right conditions
Record on the devices and in the places your users will use. That often means cheap Android phones, not studio microphones, and markets, streets, cars and offices, not quiet rooms. What matters is that every clip is checked and labelled:
- Reject clips with clipping, cut-off words, long silences or the wrong speaker.
- Tag the environment and estimated noise level so you can balance or filter later.
- Store the device, sample rate and format with each session.
Every speaker should sign an informed consent form in Khmer that explains how their voice will be used. Keep the consent records with the data: buyers, auditors and your own legal team will ask for them.
Set transcription rules before anyone transcribes
Khmer transcription has decisions that English transcription does not, and inconsistent answers to them quietly damage your model. Write a style guide that covers:
- Word boundaries. Khmer is written without spaces between words. Decide whether transcripts are segmented, and by which rules (see our guide to Khmer word segmentation).
- Numbers. Khmer digits, Arabic digits or spelled-out words, and how to write dates, times and money.
- Loanwords and code-switching. Whether English words are written in Latin script or in Khmer script, and how to spell common loanwords.
- Non-speech events. Tags for laughter, coughs, noise, music, overlapping speech and unclear words.
- Unicode. One correct character order for subscript consonants and vowels, so the same word is always stored the same way.
Native Khmer transcribers should follow this guide, and a second linguist should review every file. Measure agreement between them on a sample: if two trained people disagree often, the guide needs work, not the transcribers. Our Khmer transcription service works this way, with double review and agreement scores reported per batch.
Build a test set you can trust
Hold back a test set that you never train on, recorded from different speakers and, ideally, collected separately from your training data. Make it look like production: the same channel, noise and speaking style. Check its transcripts more carefully than the training data, because every error in the test set makes your measurements less reliable.
Measure with character error rate
Word error rate (WER) depends on where word boundaries are drawn, and for Khmer that is itself a judgement call. Character error rate (CER) avoids that problem and is the more stable metric for Khmer. Report WER too if your segmentation rules are fixed, and break both down by accent, age and environment to see where the model is weak. Independent evaluation and benchmarking helps when you compare vendors or model versions.
How much data do you need?
There is no single answer. Fine-tuning a large multilingual speech model on a narrow domain can show real gains from a few dozen hours of well-matched, accurately transcribed Khmer. A general-purpose system that must handle many accents, channels and topics needs far more. The practical approach is to start with a pilot, train, measure CER by group, and then collect more of whatever the model gets wrong: a particular province, noisy audio, or numbers.
Common mistakes to avoid
- Training and testing on the same speakers.
- Collecting only quiet, read speech for a model that will hear noisy conversation.
- Letting each transcriber invent their own rules for spacing, numbers and English words.
- Mixing Unicode orderings or legacy-font text into transcripts without cleaning.
- Losing track of consent and speaker metadata, which makes the dataset hard to use or sell later.
Where to start
If you need Khmer speech data quickly, our conversational speech, multi-accent read speech and wake-word datasets are a starting point. For anything specific to your product, our Khmer speech data collection team designs the scripts, recruits consented speakers across provinces and delivers transcribed, model-ready audio, starting with a free pilot.