You say 'tomayto'.

l found this interesting and surprising as I headed down a rabbit hole. Although Australia is more inclined to British English, it is changing.

Great Niece: Auntie Andrew, I've pressed the button for the elevator.

Me: It's called a lift here in Australia, dear child.

Great Niece: The elevator is here. 

I felt like a bag of wheat being transported in the elevator. 

What has surprised me is how many American pronunciations I use, and I guess most Australians do too. It is fairly quick from the list below to list the American pronunciations I use. 

Adult

Basil. I don't use either pronunciation. I say it like the name, or can the name be said differently too. 

Buddha

Esplanade

Evolution

Falcon, but I can remember using the English pronunciation too

Garage, but see above

Often, neither. I say orfen. 

Pasta

Privacy

Stance, I go either way, actually right in the middle.

Yoghurt

My car understands the navigation order "Home', but it can't understand me giving an address to travel to. I gave up on that.

On the home front, of course I am constantly dealing with accents. Phyllis and Kosov began by respectfully speaking only their fluent but slightly quirky English in my presence, but over time, they slipped more to using their own language, which is mostly fine with me. I've only chipped them once when we were at bakery seated at an outdoor table and they weren't speaking English. Guys, it's the three of us sitting here for brunch together, English please. It is weird how they switch from their language and English, talking together in English at times, and even to their parents on the phone. 

50 Common Words AI (and People) Mishear Across the English-Speaking World

April 30, 2025AI, LanguageLeave a comment

This post explores British and American pronunciation differences through the lens of AI speech recognition, offering new insight into how we speak and how machines listen.

As someone who teaches English phonetics in Singapore, I’m constantly navigating the subtle differences in pronunciation across accents. But nothing has opened my eyes to those differences quite like trying to get AI to understand them. Training language models has become a new lens, a strange and fascinating window, into how speech works, and where it breaks down. And although we take a deep dive into the research in this post, it’s important to appreciate how difficult this challenge really is—I know firsthand as a teacher.

You: “Set a timer for my next shed-jool.”
Siri: “Searching the web for… ‘shuttle?’”

From shed-jools to vitamins, even the smartest AIs struggle to keep up with how English is spoken around the world. The reason? Speech models aren’t trained on all English equally.

Speech AI now powers your Zoom meetings, call centers, captions, classrooms, and health apps. But these tools are overwhelmingly trained on standard American English and it shows. A Stanford-led study found that African-American speech had nearly double the word error rate (35%) compared to white American speech (19%). A separate global benchmark showed that accents from India, Britain, and France had up to 49% more errors compared to American-accented input.

Even OpenAI’s Whisper, one of the best available today, still performs better on U.S. English than on British or Australian. Meanwhile, platforms like Siri and Alexa have long struggled with Irish and Scottish accents, prompting users to change how they speak just to be understood.

This isn’t about funny mishearings, it’s about fairness, inclusion, education, and opportunity. When your accent isn’t recognized, your message often isn’t either.

And the systems aren’t being rude. They’re just undertrained.

Until models are exposed to a wider range of global speech, millions of users will keep getting subtly sidelined by systems that just don’t “hear” them.

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Why AI Still Trips Over Accents

Even today’s most advanced speech systems struggle to understand all accents equally well. Despite impressive improvements, multiple studies show consistent accent bias, particularly against non-American and non-standard dialects.

The Data: Bias in the Benchmarks

As mentioned above, a Stanford-led evaluation (Koenecke et al., 2020) of five major ASR systems—Google, Apple, IBM, Microsoft, and Amazon, found that transcripts for African American Vernacular English had a 35% word error rate (WER), nearly double the 19% for white American speakers.

In a larger audit spanning 2,700 speakers across five continents, DiChristofano et al. (2023) found WER gaps ranging from 2–12% for non-American accents, translating to up to 49% relative error. Indian, French, and Southeast Asian-accented English were among the hardest hit.

Even among native speakers, bias persists. OpenAI’s Whisper performs far better on American English than on British or Australian varieties (Graham & Roll, 2024). UK studies show that “prestige” accents like Received Pronunciation are recognized more accurately than regional dialects (Markl, 2022).

Why It Happens

Most speech models are trained predominantly on American data. As a result, the burden of intelligibility often falls on the speaker not the system.

Speech recognition models work by breaking down audio into phonemes and matching them against statistical patterns. But those patterns reflect the training data. And when that data skews heavily toward a single accent, everyone else is left misheard.

Sound Patterns That Commonly Confuse AI

Certain phonetic shifts are especially tricky for ASR systems:

• /æ/ becomes /ɑː/ in words like advertisement, patent, and pasta. Vowel shifts are unevenly represented across training corpora.

• /aɪ/ vs. /ɪ/ in words like either, neither, and vitamin. Models often guess based on spelling alone, lacking contextual accent knowledge.

• Non-rhotic /r/ drops in British English words like parliament, wrath, and version, leading to alignment errors.

• /ʃ/ vs. /sk/ in schedule (shed-yool vs. sked-jool). This cluster confusion breaks decoder predictions.

• /ɪə/ vs. /ɛ/ in leisure and niche. American decoders often miss the glide.

• T-flapping vs. T-holding in tomato and route. American English flaps the /t/; British English pronounces it clearly—confusing the ASR.

These sound mismatches often trip up models in real-world use. For instance, schedule, vitamin, and mobile are common stumbling blocks for AI depending on whether it hears British or American pronunciation.

Anecdotal Proof from the Real World

This isn’t just theory. Users have long reported needing to “code-switch” or Americanize their speech just to be understood:

• Siri and Alexa had well-documented trouble with Scottish and Irish accents.

• Google Assistant often mishears British time phrases due to differences in /t/ pronunciation.

• Contact center pilots have shown customer satisfaction increases when using accent-neutralizing AI layers.

• Reddit users frequently report toggling between British and American spellings or pronunciations to get accurate results.

Until these solutions become standard, speech recognition remains biased. For teachers, students, and users with regional or global accents, the experience can still feel exclusionary. The irony is sharp: the AI that talks back still doesn’t always listen properly.

 

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The Ultimate British vs. American Pronunciation Table

What confuses humans also confuses machines. Below is a table of over 50 words with distinct British and American pronunciations, many of which AI often misunderstands, especially when the user’s accent doesn’t match its assumptions.

Word	🇺🇸 American	🇬🇧 British
Advertisement	AD-ver-tize-ment	ad-VER-tiss-ment
Adult	AD-ult	uh-DULT
Aluminium	a-LOO-min-um	al-yuh-MIN-ee-um
Amen	AY-men	Ah-men
Asia	AY-zhuh	AY-shuh
Bald	bold	bawld
Basil	BAY-suhl	BAH-suhl
Buddha	BOOD-uh	BUD-uh
Clique	clik	cleek
Crescent	CRES-uhnt	CREZ-uhnt
Data	DAY-tuh	DAH-tuh
Dynasty	DIE-nuh-stee	DIN-uh-stee
Either	ee-thur	eye-thur
Envelope	EN-vuh-lope	ON-vuh-lope
Esplanade	ES-pluh-nard	ES-pluh-nayd
Evolution	EH-vuh-loo-shun	EE-vuh-loo-shun
Expatriate	ex-SPAY-tree-ut	ex-SPAT-ri-ut
Falcon	FAL-kun	FOL-kun
Garage	guh-RAHZH	GA-ridge
Herb	ERB	HERB
Laboratory	LAB-ruh-tor-ee	luh-BOR-uh-tree
Leisure	LEE-zher	LEZH-uh
Medicine	MED-i-sin	MED-sin
Meter	MEE-ter	MEE-tuh
Mobile	MOH-buhl	MOH-bile
Missile	MISS-uhl	MISS-eye-ul
Neither	nee-thur	nigh-thur
Niche	nitch	neesh
Oregano	uh-REG-uh-no	or-uh-GAH-no
Often	OFF-en / OFF-tuhn	same
Parliament	PAR-luh-ment	PAR-li-ment
Pasta	PAR-stuh	PAS-tuh
Patent	PAT-uhnt	PAY-tuhnt
Patronise	PAY-truh-nize	PAT-ruh-nize
Privacy	PRAI-vuh-see	PRIV-uh-see
Produce (noun)	PROH-duce	PROD-juice
Progress (noun)	PROG-ress	PROH-gress
Project (noun)	PROJ-ect	PROH-ject
Route	ROWT	ROOT
Schedule	SKED-jool	SHED-jool
Scone	skohn	skon
Semi	SEM-eye	SEM-ee
Stance	stans	starns
Tomato	tuh-MAY-to	tuh-MAH-to
Vase	vays	varz
Vendor	VEN-door	VEN-duh
Version	VER-zhun	VER-shun
Vitamin	VAI-tuh-min	VIT-uh-min
Wrath	rath	roth
Yogurt	YOH-gurt	YOG-urt
Zebra	ZEE-bruh	ZEB-ruh