Scriber — AI Medical Scribe for Behavioral Health Clinicians

If you speak English with a non-native accent, or if you see patients who speak English as a second language (or mix languages), AI transcription accuracy depends on which transcription engine you're using and how clear the audio is.

xAI and Azure Speech both handle accents well, but there are settings and techniques that improve accuracy when English isn't a first language.

Accent Handling: The Real Story

AI transcription has gotten better at non-native accents, but it's not perfect. A clinician with a Spanish, Mandarin, or Russian accent will occasionally see transcription errors where the AI mishears a word because of vowel or consonant differences between that speaker's native language and English.

Common transcription errors with accents:

Mishearing final consonants (especially /th/, /s/, /z/ sounds)
Vowel substitution (e.g., "short" becomes "shirt")
Consonant clusters (e.g., "th-r" blend in "three")

Real example: A clinician with an Indian English accent says "Is this creating stress?" The AI hears "Is dis creating stress?" (misses the "th" sound).

Real example: A clinician with a Spanish accent says "The patient reported anxiety." The AI hears "De patient reported anxiety" (the "th" becomes "d").

These aren't catastrophic errors — context makes them obvious — but they require a one-word fix in review.

Which Transcription Engine is Best for Accents?

Scriber supports multiple engines. Here's how they compare for non-native English:

xAI (Grok STT):

Decent at most accents, especially Asian and European
Relatively consistent
Handles code-switching (mixing languages) moderately well

Azure Speech Service:

Good at trained accents (British, Australian, Indian English)
Slightly less accurate on Eastern European accents
Flexible locale settings (you can set the language/accent expected)

Speechmatics:

Excellent for international accents
Built with multilingual datasets
Handles language mixing well
Slower (8–15 seconds vs. 3–5 seconds for xAI)
Good choice if accuracy is more important than speed

Recommendation: If you have a non-native accent, test xAI and Speechmatics on a 5-minute recording of your own voice. Whichever has fewer accent-related errors is your engine.

Testing Your Transcription Engine with Your Accent

Before you commit to Scriber, run this test:

Record a 5-minute clinical session (or mock session with a colleague)
Use each transcription engine available to you
Review the transcripts and count accent-related errors
Pick the engine with the fewest errors

Document the error rate. If it's under 2% (1 error per 50 words), it's acceptable. If it's over 5%, try a different engine or adjust settings.

Settings to Improve Accuracy for Non-Native Speakers

In Scriber:

Set your accent/dialect in your profile: If you have the option to specify your accent (e.g., "Indian English," "Spanish English," "ESL"), select it. This helps the AI calibrate.
Use the transcription engine that handles your accent best: Don't auto-select; choose manually.
Enable "medical/clinical" language mode if available: This helps the AI prioritize clinical terms over casual speech.

In your recording setup:

Speak clearly and slightly slower than normal: Not exaggerated, but deliberate. Accent-related errors drop significantly when your pace is moderate (140–160 words per minute instead of 180).
Position the microphone close to your mouth: 6–12 inches. This reduces competing noise and focuses the AI on your voice.
Use a headset or lavalier mic rather than a room mic: These capture your voice directly without competing ambient sound.

Patients Who Speak English as a Second Language

If your patient is a non-native English speaker, the same principles apply, but you have less control.

Common scenarios:

Patient speaks English clearly but with an accent (usually fine, transcription is accurate)
Patient code-switches (mixes languages mid-sentence): "The situación is muy complicada, you know?"
Patient speaks English but slowly and haltingly (transcription is usually accurate, just slower)

How AI handles each:

Scenario 1: Clear accent, no code-switching

Transcription accuracy: 95%+
Issue: Occasional accent-related mishearing (same as clinician with accent)
Solution: Quick review pass to catch mishears

Scenario 2: Code-switching (language mixing)

Transcription accuracy: 70–80% for the non-English language portions
Issue: The AI struggles with sudden language switches and often transcribes the non-English words phonetically
Solution: Edit the non-English portions during transcript review (or accept the phonetic transcription if the clinical content in English is clear)

Scenario 3: Slow/halting speech

Transcription accuracy: 90%+
Issue: Longer pauses can be misinterpreted; the AI may insert punctuation incorrectly
Solution: Quick review to confirm sentence boundaries

Code-Switching Workflow

If your patient code-switches (which is common in multilingual communities), here's how to handle it:

During the session:

Record normally; Scriber captures everything
You understand the code-switching because you share the languages

After the session (transcription):

Review the transcript and note where code-switching occurred
The non-English portions will likely be misspelled or phonetically transcribed
This is expected

When reviewing the AI-generated note:

Scriber's AI will include the code-switched content in the clinical note
If the meaning is unclear due to transcription errors, edit it manually
If the code-switched portion is clinically meaningful, clean it up (fix spelling, ensure meaning is clear)

Example:

Patient reported: "I'm muy estresada, you know, like the pressure is too much and mis hijos 
need me to be available and it's like impossible."

Transcription might read: "I'm moo-ay es-trah-dah, you know, like the pressure is too much and 
mees-ee-os need me to be available and it's like impossible."

In review, you'd fix to: "Patient reported feeling muy estresada [very stressed], noting that 
her children need her availability and she feels the pressure is overwhelming."

You're translating and clarifying, not just correcting transcription.

Multilingual Sessions (Both You and Patient Speaking Bilingual)

If both you and your patient are bilingual and communicating partially in another language:

Strategy 1: Conduct the session primarily in English

Easier for Scriber to transcribe
Clearer for the clinical note
If you code-switch for specific concepts, that's fine (the AI will capture it)

Strategy 2: Accept lower transcription accuracy for non-English portions

If your language mix is intentional and clinically relevant (e.g., exploring identity, language as a cultural anchor), document this in your review
Clean up the non-English transcription, ensuring clinical meaning is preserved

Strategy 3: Use a bilingual transcription service as a fallback

For highly multilingual sessions, consider having a bilingual colleague review the transcript
Or, use a human transcription service for those sessions (slower but higher accuracy)

ESL Clinician Considerations

If you're a clinician with English as a second language:

Advantage: You may have language-specific intuition about patient code-switching or language-related trauma

Challenge: Transcription errors related to accent can add 1–2 minutes to your note review

Solutions:

Test transcription engines to find the best one for your accent
Adjust your speaking pace (slower and clearer)
Improve microphone setup (close placement, headset)
Accept that you'll need a quick review pass for accent-related fixes
Build a list of terms that your engine consistently misheard (your name, patient names, clinical terms) and watch for them

Specific Language Accent Challenges

Different native languages create predictable transcription patterns. Here's what to watch for:

Spanish accent speakers:

"th" becomes "d" or "z" (e.g., "this" → "dis")
Final consonants are sometimes dropped
Vowels before /r/ are often shifted

Mandarin/Cantonese speakers:

/r/ and /l/ are confused (e.g., "clinical" → "clinicaw")
Initial consonant clusters are sometimes simplified (e.g., "stress" → "stress" usually works, but less common clusters can struggle)
Tonal information doesn't transfer, but English doesn't use tone, so this isn't usually a problem

Russian/Eastern European speakers:

/th/ is often transcribed as "s" or "z"
/j/ sound is sometimes transcribed as "y"
Stress/intonation patterns can affect word boundary detection

Indian English speakers:

Retroflexed consonants can create transcription confusion
Word stress patterns are different; sometimes affects word recognition
Overall, transcription is usually good but occasional mishears

Don't memorize these — just test your engine with your voice. If you see patterns, you can anticipate them in review.

Patient Communication About Language and Transcription

If you're using AI scribe with patients and you have an accent, or they have an accent:

Option 1: Don't mention it

Most patients don't notice
Notes are reviewed by you, so accuracy is maintained

Option 2: Be transparent

"I use an AI transcription tool to help me document. It understands most of what we say, but I always review it for accuracy. If there's something you think wasn't captured well, let me know."
This builds trust and shows you're overseeing the quality

Option 3: Adjust your process

If you feel your accent significantly impacts transcription, you could use a manual review method for specific sessions
Or, use a transcription engine that performs better on your specific accent profile

Most patients are fine with AI scribing as long as they know:

Audio is encrypted and handled securely (HIPAA)
You (the clinician) review notes for accuracy before they're finalized
You're responsible for clinical accuracy

AI Scribe for Non-Native English Speakers | Scriber Wiki