Jean-Louis Quéguiner is the Founder and CEO of Gladia. He beforehand served as Group Vice President of Knowledge, AI, and Quantum Computing at OVHcloud, one among Europe’s main cloud suppliers. He holds a Grasp’s Diploma in Symbolic AI from the College of Québec in Canada and Arts et Métiers ParisTech in Paris. Over the course of his profession, he has held important positions throughout numerous industries, together with monetary knowledge analytics, machine studying purposes for real-time digital promoting, and the event of speech AI APIs.
Gladia supplies superior audio transcription and real-time AI options for seamless integration into merchandise throughout industries, languages, and expertise stacks. By optimizing state-of-the-art ASR and generative AI fashions, it ensures correct, lag-free speech and language processing. Gladia’s platform additionally permits real-time extraction of insights and metadata from calls and conferences, supporting key enterprise use circumstances equivalent to gross sales help and automatic buyer help.
What impressed you to deal with the challenges in speech-to-text (STT) expertise, and what gaps did you see available in the market?
Once I based Gladia, the preliminary aim was broad—an AI firm that may make advanced expertise accessible. However as we delved deeper, it turned clear that voice expertise was probably the most damaged and but most important space to give attention to.
Voice is central to our every day lives, and most of our communication occurs by way of speech. But, the instruments accessible for builders to work with voice knowledge have been insufficient when it comes to velocity, accuracy, and value—particularly throughout languages.
I needed to repair that, to unpack the complexity of voice expertise and repackage it into one thing easy, environment friendly, highly effective and accessible. Builders shouldn’t have to fret concerning the intricacies of AI fashions or the nuances of context size in speech recognition. My aim was to create an enterprise-grade speech-to-text API that labored seamlessly, whatever the underlying mannequin or expertise—a real plug-and-play resolution.
What are a few of the distinctive challenges you encountered whereas constructing a transcription resolution for enterprise use?
In the case of speech recognition, velocity and accuracy—the 2 key efficiency indicators on this discipline—are inversely proportional by design. Because of this enhancing one will compromise the opposite, at the very least to some extent. The price issue, to an enormous extent, outcomes from the supplier’s alternative between velocity and high quality.
When constructing Gladia, our aim was to seek out the proper stability between these two elements, all whereas making certain the expertise stays accessible to startups and SMEs. Within the course of we additionally realized that the foundational ASR fashions like OpenAI’s Whisper, which we labored with extensively, are biased, skewering closely in the direction of English as a consequence of their coaching knowledge, which leaves numerous languages under-represented.
So, along with fixing the speed-accuracy tradeoff, it was essential to us— as a European, multilingual workforce—to optimize and fine-tune our core fashions to construct a really world API that helps companies function throughout languages.
How does Gladia differentiate itself within the crowded AI transcription market? What makes your Whisper-Zero ASR distinctive?
Our new real-time engine (Gladia Actual Time) achieves an industry-leading 300 ms latency. Along with that, it’s in a position to extract insights from a name or assembly with the so-called “audio intelligence” add-ons or options, like named entity recognition (NER) or sentiment evaluation.
To our data, only a few opponents are in a position to present each transcription and insights at such excessive latency (lower than 1s end-to-end) – and do all of that precisely in languages aside from English. Our languages help extends to over 100 languages right now.
We additionally put a particular emphasis on making the product actually stack agnostic. Our API is suitable with all present tech stacks and telephony protocols, together with SIP, VoIP, FreeSwitch and Asterisk. Telephony protocols are particularly advanced to combine with, so we consider this product side can deliver great worth to the market.
Hallucinations in AI fashions are a major concern, particularly in real-time transcription. Are you able to clarify what hallucinations are within the context of STT and the way Gladia addresses this drawback?
Hallucination normally happens when the mannequin lacks data or doesn’t have sufficient context on the subject. Though fashions can produce outputs tailor-made to a request, they will solely reference data that existed on the time of their coaching, and that is probably not up-to-date. The mannequin will create coherent responses by filling in gaps with data that sounds believable however is inaccurate.
Whereas hallucinations turned recognized within the context of LLMs first, they happen with speech recognition fashions— like Whisper ASR, a number one mannequin within the discipline developed by OpenAI – as properly. Whisper’s hallucinations are like these of LLMs as a consequence of the same structure, so it’s an issue that considerations generative fashions, which can be in a position to predict the phrases that comply with primarily based on the general context. In a method, they ‘invent’ the output. This method may be contrasted with extra conventional, acoustic-based ASR architectures that match the enter sound to output in a extra mechanical method
Consequently, you might discover phrases in a transcript that weren’t really stated, which is clearly problematic, particularly in fields like medication, the place a mistake of this type can have grave penalties.
There are a number of strategies to handle and detect hallucinations. One widespread method is to make use of a retrieval-augmented technology (RAG) system, which mixes the mannequin’s generative capabilities with a retrieval mechanism to cross-check info. One other methodology entails using a “chain of thought” method, the place the mannequin is guided by way of a sequence of predefined steps or checkpoints to make sure that it stays on a logical path.
One other technique for detecting hallucinations entails utilizing methods that assess the truthfulness of the mannequin’s output throughout coaching. There are benchmarks particularly designed to judge hallucinations, which contain evaluating totally different candidate responses generated by the mannequin and figuring out which one is most correct.
We at Gladia have experimented with a mixture of methods when constructing Whisper-Zero, our proprietary ASR that removes nearly all hallucinations. It’s confirmed wonderful ends in asynchronous transcription, and we’re at the moment optimizing it for real-time to attain the identical 99.9% data constancy.
STT expertise should deal with a variety of complexities like accents, noise, and multi-language conversations. How does Gladia method these challenges to make sure excessive accuracy?
Language detection in ASR is a particularly advanced process. Every speaker has a singular vocal signature, which we name options. By analyzing the vocal spectrum, machine studying algorithms can carry out classifications, utilizing the Mel Frequency Cepstral Coefficients (MFCC) to extract the primary frequency traits.
MFCC is a technique impressed by human auditory notion. It’s a part of the “psychoacoustic” discipline, specializing in how we understand sound. It emphasizes decrease frequencies and makes use of methods like normalized Fourier decomposition to transform audio right into a frequency spectrum.
Nonetheless, this method has a limitation: it is primarily based purely on acoustics. So, if you happen to converse English with a robust accent, the system might not perceive the content material however as a substitute decide primarily based in your prosody (rhythm, stress, intonation).
That is the place Gladia’s revolutionary resolution is available in. We have developed a hybrid method that mixes psycho-acoustic options with content material understanding for dynamic language detection.
Our system would not simply take heed to the way you converse, but additionally understands what you are saying. This twin method permits for environment friendly code-switching and would not let robust accents get misrepresented/misunderstood.
Code-switching—which is amongst our key differentiators—is a very essential function in dealing with multilingual conversations. Audio system might change between languages mid-conversation (and even mid-sentence), and the flexibility of the mannequin to transcribe precisely on the fly regardless of the change is crucial.
Gladia API is exclusive in its skill to deal with code-switching with this many language pairs with a excessive stage of accuracy and performs properly even in noisy environments, recognized to scale back the standard of transcription.
Actual-time transcription requires ultra-low latency. How does your API obtain lower than 300 milliseconds latency whereas sustaining accuracy?
Protecting latency below 300 milliseconds whereas sustaining excessive accuracy requires a multifaceted method that blends {hardware} experience, algorithm optimization, and architectural design.
Actual-time AI isn’t like conventional computing—it’s tightly linked to the ability and effectivity of GPGPUs. I’ve been working on this house for practically a decade, main the AI division at OVHCloud (the most important cloud supplier within the EU), and realized firsthand that it’s all the time about discovering the precise stability: how a lot {hardware} energy you want, how a lot it prices, and the way you tailor the algorithms to work seamlessly with that {hardware}.
Efficiency in actual time AI comes from successfully aligning our algorithms with the capabilities of the {hardware}, making certain each operation maximizes throughput whereas minimizing delays.
However it’s not simply the AI and {hardware}. The system’s structure performs an enormous position too, particularly the community, which might actually affect latency. Our CTO, who has deep experience in low-latency community design from his time at Sigfox (an IoT pioneer), has optimized our community setup to shave off useful milliseconds.
So, it’s actually a mixture of all these elements—sensible {hardware} decisions, optimized algorithms, and community design—that lets us persistently obtain sub-300ms latency with out compromising on accuracy.
Gladia goes past transcription with options like speaker diarization, sentiment evaluation, and time-stamped transcripts. What are some revolutionary purposes you’ve seen your shoppers develop utilizing these instruments?
ASR unlocks a variety of purposes to platforms throughout verticals, and it’s been superb to see what number of actually pioneering firms have emerged within the final two years, leveraging LLMs and our API to construct cutting-edge, aggressive merchandise. Listed below are some examples:
- Sensible note-taking: Many consumers are constructing instruments for professionals who must rapidly seize and set up data from work conferences, scholar lectures, or medical consultations. With speaker diarization, our API can establish who stated what, making it simple to comply with conversations and assign motion gadgets. Mixed with time-stamped transcripts, customers can soar straight to particular moments in a recording, saving time and making certain nothing will get misplaced in translation.
- Gross sales enablement: Within the gross sales world, understanding buyer sentiment is every thing. Groups are utilizing our sentiment evaluation function to realize real-time insights into how prospects reply throughout calls or demos. Plus, time-stamped transcripts assist groups revisit key components of a dialog to refine their pitch or handle consumer considerations extra successfully. For this use case particularly, NER can be key to figuring out names, firm particulars, and different data that may be extracted from gross sales calls to feed the CRM robotically.
- Name heart help: Corporations within the contract heart house are utilizing our API to supply reside help to brokers, in addition to flagging buyer sentiment throughout calls. Speaker diarization ensures that issues being stated are assigned to the precise individual, whereas time-stamped transcripts allow supervisors to overview crucial moments or compliance points rapidly. This not solely improves the client expertise – with higher on-call decision price and high quality monitoring – but additionally boosts agent productiveness and satisfaction.
Are you able to focus on the position of customized vocabularies and entity recognition in enhancing transcription reliability for enterprise customers?
Many industries depend on specialised terminology, model names, and distinctive language nuances. Customized vocabulary integration permits the STT resolution to adapt to those particular wants, which is essential for capturing contextual nuances and delivering output that precisely displays your small business wants. As an example, it lets you create a listing of domain-specific phrases, equivalent to model names, in a selected language.
Why it’s helpful: Adapting the transcription to the precise vertical lets you reduce errors in transcripts, attaining a greater person expertise. This function is particularly crucial in fields like medication or finance.
Named entity recognition (NER) extracts and identifies key data from unstructured audio knowledge, equivalent to names of individuals, organizations, places, and extra. A typical problem with unstructured knowledge is that this crucial data isn’t readily accessible—it is buried throughout the transcript.
To unravel this, Gladia developed a structured Key Knowledge Extraction (KDE) method. By leveraging the generative capabilities of its Whisper-based structure—much like LLMs—Gladia’s KDE captures context to establish and extract related data instantly.
This course of may be additional enhanced with options like customized vocabulary and NER, permitting companies to populate CRMs with key knowledge rapidly and effectively.
In your opinion, how is real-time transcription remodeling industries equivalent to buyer help, gross sales, and content material creation?
Actual-time transcription is reshaping these industries in profound methods, driving unbelievable productiveness positive factors, coupled with tangible enterprise advantages.
First, real-time transcription is a game-changer for help groups. Actual-time help is essential to enhancing the decision price due to quicker responses, smarter brokers, and higher outcomes (when it comes to NSF, deal with instances, and so forth). As ASR methods get higher and higher at dealing with non-English languages and performing real-time translation, contact facilities can obtain a really world CX at decrease margins.
In gross sales, velocity and spot-on insights are every thing. Equally to what occurs with name brokers, real-time transcription is what equips them with the precise insights on the proper time, enabling them to give attention to what issues probably the most in closing offers.
For creators, real-time transcription is probably much less related right now, however nonetheless filled with potential, particularly relating to reside captioning and translation throughout media occasions. Most of our present media prospects nonetheless choose asynchronous transcription, as velocity is much less crucial there, whereas accuracy is essential for purposes like time-stamped video enhancing and subtitle technology.
Actual-time AI transcription appears to be a rising pattern. The place do you see this expertise heading within the subsequent 5-10 years?
I really feel like this phenomenon, which we now name real-time AI, goes to be in every single place. Primarily, what we actually discuss with right here is the seamless skill of machines to work together with folks, the way in which we people already work together with each other.
And if you happen to have a look at any Hollywood film (like Her) set sooner or later, you’ll by no means see anybody there interacting with clever methods by way of a keyboard. For me, that serves as the final word proof that within the collective creativeness of humanity, voice will all the time be the first method we work together with the world round us.
Voice, as the primary vector to mixture and share human data, has been a part of human tradition and historical past for for much longer than writing. Then, writing took over as a result of it enabled us to protect our data extra successfully than counting on the group elders to be the guardians of our tales and knowledge.
GenAI methods, able to understanding speech, producing responses, and storing our interactions, introduced one thing utterly new to the house. It’s the most effective of each phrases and the most effective of humanity actually. It provides us this distinctive energy and power of voice communication with the advantage of reminiscence, which beforehand solely written media may safe for us. For this reason I consider it’s going to be in every single place – it is our final collective dream.
Thanks for the good interview, readers who want to be taught extra ought to go to Gladia.