Category: Technology

A robot reading a script. The text-to-speech voices at ElevenLabs certainly sound intelligent as well as natural!

ElevenLabs : 5-Star Tool for Language Work and Study

If you’re a regular reader, you’ll know how impressed I’ve been at ElevenLabs, the text-to-speech creator that stunned the industry when its super-realistic voices were unleashed on the world. Since then, it’s made itself irreplaceable in both my work and study, and it bears spreading the word again: ElevenLabs is a blow-your-socks-off kind of tool for creating spoken audio content.

Professional Projects

In my work developing language learning materials for schools, arranging quality narration used to involve coordinating with agencies and studios — a process that was both time-consuming and costly. We’ve had issues with errors, too, which cost a project time with re-recordings. And that’s not to mention the hassle keeping sections up-to-date. Removing ‘stereo’ from an old vocab section (who has those now?) would usually trigger a complete re-record.

With ElevenLabs, I can now produce new sections promptly, utilising its impressive array of voices across multiple languages. The authenticity and clarity of these voices are fantastic – I really can’t understate it – and it’s made maintaining the biggest language learning site for schools so much easier.

Supporting Individual Learning

As a language learner, ElevenLabs is more than worth its salt, too. It’s particularly good for assembling short listening passages – about a minute long – to practise ‘conversation islands’—a well-regarded polyglot technique for achieving conversational fluency.

Beyond language learning, the tool can be a great support to other academic projects. I’ve created concise narrations of complex topics, converting excerpts from scholarly papers into audio format. Listening to these clips in spare moments (or even in the background while washing up) has helped cement some key concepts, and prime my mind for conventional close study.

Flexible and Affordable Plans

ElevenLabs offers a range of pricing options to suit different needs:

• Free Plan: Ideal for those starting out, this plan provides 10,000 characters per month, roughly equating to 10 minutes of audio.

• Starter Plan: At £5 per month, you receive 30,000 characters (about 30 minutes of audio), along with features like voice cloning and commercial use rights.

• Creator Plan: For £22 per month, this plan offers 100,000 characters (around 100 minutes of audio), plus professional voice cloning and higher-quality outputs.

For messing around, that free plan is not too stingy at all – you can really get a feel for the tool from it. Personally, I’ve not needed to move beyond the starter plan yet, which is pretty much a bargain at around a fiver a month.

Introducing ElevenReader

And there’s more! Complementing the TTS service, ElevenLabs has introduced ElevenReader, a free tool that narrates PDFs, ePubs, articles, and newsletters in realistic AI voices. Available on both iOS and Android platforms, the app doesn’t even consume credits from your ElevenLabs subscription plan.

Seriously, I can’t even believe this is still free – go and try it!

Final Thoughts

ElevenLabs has truly transformed the way I create and consume spoken content. It truly is my star tool from the current crop of AI-powered utilities.

The ElevenLabs free tier is enough for most casual users to have a dabble – go and try it today!

A robot playwright - now even more up-to-date with SearchGPT.

Topical Dialogues with SearchGPT

As if recent voice improvements weren’t enough of a treat, OpenAI has just introduced another killer feature to ChatGPT, one that can likewise beef up your custom language learning resources. SearchGPT enhances the LLM’s ability to access and incorporate bang up-to-date information from the web.

It’s a development that is particularly beneficial for language learners seeking to create study materials that reflect current events and colloquial language use. With few exceptions until now, LLMs like ChatGPT have had a ‘data cutoff’, thanks to mass text training having an end-point (albeit a relatively recent one). Some LLMs, like Microsoft’s Copilot, have introduced search capabilities, but their ability to retrieve truly current data could be hit and miss.

With SearchGPT, OpenAI appear to have cracked search accuracy a level to rival AI search tool Perplexity – right in the ChatGPT app. And it’s as simple as highlighting the little world icon that you might already have noticed under the prompt field.

The new SearchGPT icon in the ChatGPT prompt bar.

The new SearchGPT icon in the ChatGPT prompt bar.

Infusing Prompts with SearchGPT

Switching this on alongside tried-and-tested language learning prompt techniques yields some fun – and pedagogically useful – results. For instance, you can prompt ChatGPT to generate dialogues or reading passages based on the latest news from your target language country/ies. Take this example:

A language learning dialogue on current affairs in German, beefed up by OpenAI's SearchGPT

A language learning dialogue on current affairs in German, beefed up by OpenAI’s SearchGPT

SearchGPT enables content that mirrors real-life discussion with contemporary vocabulary and expressions (already something it was great at). But it also incorporates accurate, up-to-the-minute, and even cross-referenced information. That’s a big up for transparency.

Unsure where that info came from? Just click the in-text links!

Enhancing Speaking Practice with Authentic Contexts

Beyond reading, these AI-generated dialogues serve as excellent scripts for speaking practice. Learners can role-play conversations, solo or group-wise, to improve pronunciation, intonation, and conversational flow. This method bridges the gap between passive understanding and active usage, a crucial step in achieving fluency.

Incorporating SearchGPT into your language learning content creation toolbox reconnects your fluency journey with the real, evolving world. Have you used it yet? 

Apples and oranges, generated by Google's new image algorithm Imagén 3

Google’s Imagén 3 : More Reliable Text for Visual Resources

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If you use AI imaging for visual teaching resources, but decry its poor text handling, then Google might have cracked it. Their new algorithm for image generation, Imagén 3, is much more reliable at including short texts without errors.

What’s more, the algorithm is included in the free tier of Google’s LLM, Gemini. Ideal for flashcards and classroom posters, you now get quite reliable results when prompting for Latin-alphabet texts on the platform. Image quality seems to have improved too, with a near-photographic finish possible:

A flashcard produced with Google Gemini and Imagén 3.

A flashcard produced with Google Gemini and Imagén 3.

The new setup seems marginally better at consistency of style, too. Here’s a second flashcard, prompting for the same style. Not quite the same font, but close (although in a different colour).

A flashcard created with Google Gemini and Imagén 3.

A flashcard created with Google Gemini and Imagén 3.

It’s also better at real-world details like flags. Prompting in another engine for ‘Greek flag’, for example, usually results in some terrible approximation. Not in Imagén 3 – here are our apples and oranges on a convincing Greek flag background:

Apples and oranges on a square Greek flag, generated by Google's Imagén 3

Apples and oranges on a square Greek flag, generated by Google’s Imagén 3

It’s not perfect, yet. For one thing, it performed terribly with non-Latin alphabets, producing nonsense each time I tested it. And while it’s great with shorter texts, it does tend to break down and produce the tell-tall typos with anything longer than a single, short sentence. Also, if you’re on the free tier, it won’t allow you to create images of human beings just yet.

That said, it’s a big improvement on the free competition like Bing’s Image Creator. Well worth checking out if you have a bunch of flashcards to prepare for a lesson or learning resource!

Greek text on a packet of crisps

Language Lessons from Packaging (And A Little Help from ChatGPT)

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If you love scouring the multilingual packaging of household products from discounter stores (a niche hobby, I must admit, even for us linguists), then  there’s a fun way to automate it with LLMs like ChatGPT.

Take the back of this packet of crisps. To many, a useless piece of rubbish. To me (and some of you, I hope!), a treasure of language in use.

Greek text on a packet of crisps - food and household item packaging can be a great source of language in use.

Greek text on a packet of crisps

Normally, I’d idly read through these, looking up any unfamiliar words in a dictionary. But, using an LLM app with an image facility like ChatGPT, you can automate that process. What’s more, you can request all sorts of additional info like dictionary forms, related words, and so on.

From Packaging to Vocab List

Take a snap of your packaging, and try this prompt for starters:

Create a vocabulary list from the key content words on the packaging label. For each word, list:
– its dictionary form
– a new, original sentence illustrating the word in use
– common related words

The results should be an instantly useful vocab list with added content for learning:

Vocabulary list from food packaging by ChatGPT

Vocabulary list compiled by ChatGPT from a food packaging label

I added a note-taking stage to round it off. It always helps me to write down what I’m learning, adding a kinaesthetic element to the visual (and aural, if you’ve had ChatGPT speak its notes out loud). Excuse the scrawl… (As long as your notes are readable by you, they’re just fine!)

Handwritten vocabulary notes derives from crisp packet packaging

Notes on a crisp packet…

It’s a fun workflow that really underscores the fact that there are free language lessons all around us.

Especially in the humblest, and often least glamorous, of places.

ChatGPT takes conversation to the next level with Advanced Voice Mode

ChatGPT Advanced Voice Mode is Finally Here (For Most of Us!)

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Finally – and it has taken SO much longer to get it this side of the Pond – Advanced Voice Mode has popped up in my ChatGPT. And it’s a bit of a mind-blower to say the least.

Multilingually speaking, it’s a huge step up for the platform. For a start, its non-English accents are hugely improved – no longer French or German with an American twang. Furthermore, user language detection seems more reliable, too. Open it up, initiate a conversation in your target language, and it’s ready to go without further fiddling.

But it’s the flexibility and emotiveness of those voices which is the real game-changer. There’s real humanity in those voices, now, reminiscent of Hume’s emotionally aware AI voices. As well as emotion, there’s variation in timbre and speed. What that means for learners is that it’s now possible to get it to mimic slow, deliberate speech when you ask that language learning staple “can you repeat that more slowly, please?”. It makes for a much more adaptive digital conversation partner.

Likewise – and rather incredibly – it’s possible to simulate a whole range of regional accents. I asked for Austrian German, and believe me, it is UNCANNILY good. Granted, it did occasionally verge on parody, but as a general impression, it’s shocking how close it gets. It’s a great way to prepare for speaking your target language with real people, who use real, regionally marked speech.

Advanced Voice Mode, together with its recently added ability to remember details from past conversations (previously achievable only via a hack), is turning ChatGPT into a much cannier language learning assistant. It was certainly worth the wait. And for linguaphiles, it’ll be fascinating to see how it continues to develop as an intelligent conversationalist from here.

Mapping out conversational probabilities - it's much easier with flowcharts.

Vocabulary Flowcharts : Preparing for Probabilities with ChatGPT

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The challenge in preparing for a speaking task in the wild is that you’re dealing with multiple permutations. You ask your carefully prepared question, and you get any one of a number of likely responses back. That, in turn, informs your next question or reply, and another one-of-many comebacks follows.

It’s probability roulette.

What if you could map all of these conversational pathways out, though? Flowcharts have long been the logician’s tool of choice for visualising processes that involve forking choices. Combined with generative AI’s penchant for assembling real-world language, we have a recipe for much more dynamic language prep resources than a traditional vocab list.

And, thanks to a ready-made flowchart plugin for ChatGPT – courtesy of the charting folks at Whimsical.com – it’s really easy to knock one together.

Vocabulary Flowcharts in Minutes

In your ChatGPT account, you’ll need to locate the Whimsical GPT. Then, it’s just a case of detailing the conversational scenario you want to map out. Here’s an example for ‘opening a bank account in Germany’:

Create a flowchart detailing different conversational choices and paths in German for the scenario “Opening a bank account as a non-resident of Germany planning to work there for six months.” Include pathways for any problems that might occur in the process. Ensure all the text reflects formal, conversational German.

The result should be a fairly detailed ‘probability map’ of conversational turns:

A 'vocabulary flowchart' in German, created by the Whimsical.com GPT on ChatGPT.

A ‘vocabulary flowchart’ in German, created by the Whimsical.com GPT on ChatGPT.

Vocabulary flowcharts are another tool in your AI arsenal for speaking prep. Have you given them a whirl yet? Tell us about your own prep in the comments!

Lots of colourful neon shortcuts on a screen.

Apple Shortcuts for Smart Study Hacks

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I’ve been optimising my iPad for study recently, trying to make it a portable, one-stop shop for reading and prep on-the-move. It’s no doubt another flex of my childhood awe for Inspector Gadget’s niece Penny, and her computer-in-a-book that could do everything.

Anyway, it’s led me to discover lots of features I half knew about, but had ignored for the duration of my Apple fanboydom. And one of the biggest revelations of rediscovery has been the Apple Shortcuts app.

In a nutshell, Shortcuts allows you to bundle all sorts of custom process chains into a single, iconisable action. For example, a shortcut could retrieve something, do something with the result, then present it in a certain way. People use them for all sorts of admin tasks – collecting stock prices and collating them in a spreadsheet with a single click, for instance.

The cool thing is that many third-party apps have extensions that you can link into shortcuts. ChatGPT, for example, which I find invaluable as a quick summary or explanatory tool, can be part of an action chain. And you can trigger chains not just with clicks, but from documents, via the share link.

Shortcuts for Smart Study : Brief Description

Using the app, I put together a quick shortcut I called “Brief Description”. It sends the current PDF (from a browser window, or from the Files app), to ChatGPT, prompting it for a one-paragraph summary.

A screenshot from Apple Shortcuts of a 'Brief Explanation' action using ChatGPT.

A screenshot from Apple Shortcuts of a ‘Brief Explanation’ action using ChatGPT.

As you can see, one of the best things about it is how you assemble a shortcut in more or less natural language, as you select items via an intuitive click-and-build interface. There are also plenty of resources for getting started and pushing the boundaries of it (like this very clued-up YouTube channel!).

The result of my shortcut offers a great way to get a paper summary, whatever the language:

Screenshot of an Apple Shortcuts link in the share menu.

Screenshot of an Apple Shortcuts link in the share menu.

When working through a bunch of resources, it’s great to ascertain in a click or two whether it might be a worthwhile full read, making this a brilliant time-saver. It’s even more powerful when combined with ChatGPT’s new(ish) memory features – my account ‘knows’ what I’ve been working on from recent chats, so is even better able to judge what’s useful.

For sure, I’ll be exploring more of this over the coming weeks, as it’s clear I’ve barely scratched the surface of how useful it could be yet. Have you used Apple Shortcuts to create smart study hacks? Let us know in the comments!

ElevenLabs Reader is the TTS robot you need!

ElevenLabs Reader : Enjoy it while it’s free!

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I’ve raved before about the fantastic text-to-speech facility at ElevenLabs. Voices so realistic they pretty much pass for human, that work across languages, and can even clone your own.

Well, there’s a little-feted app that the ElevenLabs team are behind that is a similar show-stopper in its category. It’s simply called Reader, and is a free text-to-speech narration app for Android and iOS. 

Like the company’s web-based TTS service, it has the same range of emotive, expressive, ultra-realistic voices. It can also cope well narrating many languages, which are autodetected. Best of all for me is that it links with your device’s documents, so I can quickly import the papers I’m reading. Listening AND reading has made a massive difference to my comprehension and recall – multimodal is definitely the way forward for me!

You do get the odd strange artefact in the readout, but the product is still in Beta (likely the reason it’s currently completely free), and glitches are rare. What I’m also missing in it is the ability to tweak imported texts in-app, as you can do with Speechify. This would allow some cleaning of the file pre-narration (I lost count of the number of times I had to skip the page footer DOI link, which it hilariously mis-narrated many times). You can, of course, simply export and clean the files as text before importing, which gets round that.

In any case, they’re worth putting up with while the app is still free and in Beta. Even as it is, you have features here that you’d pay a small fortune for in other apps (ahem, Speechify). Definitely worth a punt if you’re looking for TTS support in your reading! Find download links for Android and iOS here.

Shelves of helpful robots - a bit like Poe, really!

Which LLM? Poe offers them all (and some!)

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One of the most frequent questions when I’ve given AI training to language professionals is “which is your favourite platform?”. It’s a tricky one to answer, not least because we’re currently in the middle of the AI Wars – new, competing models are coming out all the time, and my personal choice of LLM changes with each new release.

That said, I’m a late and recent convert to Poe – an app that gives you them all in one place. The real clincher is the inclusion of brand new models, before they’re widely available elsewhere.

To illustrate just how handy it is, just a couple of weeks ago, Meta dropped Llama 3.1 – the first of their models to really challenge the frontrunners. However, unless you have a computer powerful enough to run it locally, or access to Meta AI (US-only right now), you’ll be waiting a while to try it.

Enter Poe. Within a couple of days, all flavours of Llama 3.1 were available. And the best thing? You can interact with most of them for nothing.

The Poe Currency

Poe works on a currency of Compute Points, which are used to pay for messages to the model. More powerful models guzzle through compute points at a higher rate, and models tend to become cheaper as they get older. Meta’s Llama-3.1-405B-T, for example, costs 335 points per message, while OpenAI’s ChatGPT-4o-Mini comes in at a bargain 15 points for each request.

Users of Poe’s free tier get a pretty generous 3000 Compute Points every day. That’s enough credit to work quite extensively on some of the older models without much limitation at all. But it’s also enough to get some really useful (8-ish-requests daily) use from Llama 3.1. And, thanks to that, I can tell you – Llama 3.1 is great at creating language learning resources!

Saying that, with the right prompt, most of the higher-end models are, these days. Claude-3.5-Sonnet is another favourite – check out my interactive worksheet experiments with it here. And yes, Claude-3.5-Sonnet is available on Poe, at a cost of 200 points per message (and that’s already dropped from its initial cost some weeks back!). Even the image generation model Flux has made its way onto the platform, just days after the hype. And it’s a lot better with text-in-image (handy if you’re creating illustrated language materials).

Poe pulls together all sorts of cloud providers in a marketplace-style setup to offer the latest bots, and it’s a model that works. The latest and greatest will always burn through your stash of Computer Points faster, but there’s still no easier way to be amongst the first to try a new LLM!

AI Parallel Texts for Learning Two Similar Languages

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I’ve seen a fair few social media posts recently about linguist Michael Petrunin’s series of Comparative Grammars for polyglots. They seem to have gone down a storm, not least because of the popularity of triangulation as a polyglot strategy.

They’re a great addition to the language learning bookshelf, since there’s still so little formal course material that uses this principle. Of course, you can triangulate by selecting course books in your base language, as many do with Assimil and other series like the Éditions Ellipse.

Parallel Texts à la LLM

But LLMs like ChatGPT, which already do a great job of the parallel text learning style, are pretty handy for creative comparative texts, too. Taking a story format, here’s a sample parallel text prompt for learners of German and Dutch. It treats each sentence as a mini lesson in highlighting differences between the languages.

I’m learning Dutch and German, two closely related languages. To help me learn them in parallel and distinguish them from each other, create a short story for me in Dutch, German and English in parallel text style. Each sentence should be given in Dutch, German and English. Purposefully use grammatical elements which highlight the differences between the languages, which a student of both does need to work hard to distinguish, in order to make the text more effective.

The language level should be lower intermediate, or B1 on the CEFR scale. Make the story engaging, with an interesting twist. Format the text so it is easy to read, grouping the story lines together with each separate sentence on a new line, and the English in italics.

You can tweak the formatting, as well as the premise – specify that the learner already speaks one of the languages more proficiently than the other, for example. You could also offer a scenario for the story to start with, so you don’t end up with “once upon a time” every run. But the result is quite a compact, step-by-step learning resource that builds on a comparative approach.

ChatGPT creating parallel texts in German and Dutch with an English translation.

ChatGPT creating parallel texts in German and Dutch with an English translation.

Variations and Limitations

I also tried prompting for explanatory notes:

Where the languages differ significantly in grammar / syntax, add an explanatory note (in English) to the sentences, giving details.

This was very hit and miss, with quite unhelpful notes in most runs. In fact, this exposes the biggest current limitation of LLMs: they’re excellent content creators, but still far off the mark in terms of logically appraising the language they create.

It is, however, pretty good at embellishing the format of its output. The following variation is especially impressive in an LLM platform that shows a preview of its code:

I’m learning Spanish and Portuguese, two closely related languages. To help me learn them in parallel and distinguish them from each other, create a short story for me in Spanish, Portuguese and English in parallel text style. Each sentence should be given in Spanish, Portuguese and English. Purposefully use grammatical elements which highlight the differences between the languages, which a student of both does need to work hard to distinguish, in order to make the text more effective.

The language level should be lower intermediate, or B1 on the CEFR scale. Make the story engaging, with an interesting twist.

The output should be an attractively formatted HTML page, using a professional layout. Format the sentences so they are easy to read, grouping the story lines together with each separate sentence on a new line, and the English in italics. Hide the English sentences first – include a “toggle translation” button for the user.

Claude by Anthropic creating an HTML-formatted parallel story in Spanish and Portuguese.

Claude by Anthropic creating an HTML-formatted parallel story in Spanish and Portuguese.

It’s another use case that highlights LLMs’ greatest strength: the creation of humanlike texts. For linguists, it matters not a jot how much (or little) deep understanding there is beneath that. With the language quality now almost indistinguishable from real people-speak, AI texts serve as brilliant ‘fake authentic’ language models.

e-Stories as parallel texts are yet another fun, useful flavour of that!