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 in all 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 vital positions throughout numerous industries, together with monetary information analytics, machine studying functions for real-time digital promoting, and the event of speech AI APIs.
Gladia offers superior audio transcription and real-time AI options for seamless integration into merchandise throughout industries, languages, and know-how 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 assist.
What impressed you to deal with the challenges in speech-to-text (STT) know-how, and what gaps did you see out there?
After I based Gladia, the preliminary aim was broad—an AI firm that might make advanced know-how accessible. However as we delved deeper, it turned clear that voice know-how was essentially the most damaged and but most crucial space to concentrate on.
Voice is central to our every day lives, and most of our communication occurs by means of speech. But, the instruments accessible for builders to work with voice information have been insufficient by way of pace, accuracy, and value—particularly throughout languages.
I wished to repair that, to unpack the complexity of voice know-how 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 know-how—a real plug-and-play resolution.
What are a number of the distinctive challenges you encountered whereas constructing a transcription resolution for enterprise use?
Relating to speech recognition, pace and accuracy—the 2 key efficiency indicators on this area—are inversely proportional by design. Because of this enhancing one will compromise the opposite, not less than to some extent. The associated fee issue, to a giant extent, outcomes from the supplier’s selection between pace and high quality.
When constructing Gladia, our aim was to seek out the right stability between these two elements, all whereas making certain the know-how 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 attributable to their coaching information, which leaves a number of languages under-represented.
So, along with fixing the speed-accuracy tradeoff, it was essential to us— as a European, multilingual crew—to optimize and fine-tune our core fashions to construct a very 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 information, 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 apart from English. Our languages assist extends to over 100 languages as we speak.
We additionally put a particular emphasis on making the product really stack agnostic. Our API is appropriate with all current 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 carry great worth to the market.
Hallucinations in AI fashions are a big 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 information or doesn’t have sufficient context on the subject. Though fashions can produce outputs tailor-made to a request, they’ll 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 wrong.
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 area developed by OpenAI – as effectively. Whisper’s hallucinations are like these of LLMs attributable to an analogous structure, so it’s an issue that issues generative fashions, which are in a position to predict the phrases that comply with primarily based on the general context. In a method, they ‘invent’ the output. This strategy might be contrasted with extra conventional, acoustic-based ASR architectures that match the enter sound to output in a extra mechanical method
In consequence, you could discover phrases in a transcript that weren’t really mentioned, which is clearly problematic, particularly in fields like drugs, the place a mistake of this sort can have grave penalties.
There are a number of strategies to handle and detect hallucinations. One frequent strategy 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 details. One other methodology entails using a “chain of thought” strategy, the place the mannequin is guided by means 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 programs that assess the truthfulness of the mannequin’s output throughout coaching. There are benchmarks particularly designed to guage 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 mix of methods when constructing Whisper-Zero, our proprietary ASR that removes just about all hallucinations. It’s confirmed glorious leads to asynchronous transcription, and we’re presently optimizing it for real-time to attain the identical 99.9% data constancy.
STT know-how should deal with a variety of complexities like accents, noise, and multi-language conversations. How does Gladia strategy these challenges to make sure excessive accuracy?
Language detection in ASR is a particularly advanced activity. 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 principle frequency traits.
MFCC is a technique impressed by human auditory notion. It’s a part of the “psychoacoustic” area, 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.
Nevertheless, this strategy has a limitation: it is primarily based purely on acoustics. So, in case you converse English with a powerful accent, the system could not perceive the content material however as an alternative decide primarily based in your prosody (rhythm, stress, intonation).
That is the place Gladia’s modern resolution is available in. We have developed a hybrid strategy 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 strategy 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 characteristic in dealing with multilingual conversations. Audio system could change between languages mid-conversation (and even mid-sentence), and the power of the mannequin to transcribe precisely on the fly regardless of the change is essential.
Gladia API is exclusive in its capability to deal with code-switching with this many language pairs with a excessive stage of accuracy and performs effectively 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?
Maintaining latency underneath 300 milliseconds whereas sustaining excessive accuracy requires a multifaceted strategy that blends {hardware} experience, algorithm optimization, and architectural design.
Actual-time AI isn’t like conventional computing—it’s tightly linked to the facility and effectivity of GPGPUs. I’ve been working on this house for almost a decade, main the AI division at OVHCloud (the most important cloud supplier within the EU), and discovered firsthand that it’s all the time about discovering the fitting 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.
But it surely’s not simply the AI and {hardware}. The system’s structure performs a giant function too, particularly the community, which might actually influence 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 priceless milliseconds.
So, it’s actually a mixture of all these elements—sensible {hardware} selections, optimized algorithms, and community design—that lets us constantly 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 modern functions you’ve seen your shoppers develop utilizing these instruments?
ASR unlocks a variety of functions to platforms throughout verticals, and it’s been wonderful to see what number of really 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 purchasers are constructing instruments for professionals who must shortly seize and set up data from work conferences, scholar lectures, or medical consultations. With speaker diarization, our API can determine who mentioned what, making it simple to comply with conversations and assign motion objects. Mixed with time-stamped transcripts, customers can bounce 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 characteristic to achieve real-time insights into how prospects reply throughout calls or demos. Plus, time-stamped transcripts assist groups revisit key elements of a dialog to refine their pitch or deal with consumer issues extra successfully. For this use case particularly, NER can also be key to figuring out names, firm particulars, and different data that may be extracted from gross sales calls to feed the CRM mechanically.
- Name heart help: Corporations within the contract heart house are utilizing our API to offer dwell help to brokers, in addition to flagging buyer sentiment throughout calls. Speaker diarization ensures that issues being mentioned are assigned to the fitting individual, whereas time-stamped transcripts allow supervisors to evaluation essential moments or compliance points shortly. This not solely improves the client expertise – with higher on-call decision charge and high quality monitoring – but additionally boosts agent productiveness and satisfaction.
Are you able to talk about the function 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 corporation wants. For example, it lets you create an inventory of domain-specific phrases, equivalent to model names, in a selected language.
Why it’s helpful: Adapting the transcription to the particular vertical lets you decrease errors in transcripts, attaining a greater consumer expertise. This characteristic is very essential in fields like drugs or finance.
Named entity recognition (NER) extracts and identifies key data from unstructured audio information, equivalent to names of individuals, organizations, areas, and extra. A standard problem with unstructured information is that this essential data isn’t readily accessible—it is buried inside the transcript.
To unravel this, Gladia developed a structured Key Knowledge Extraction (KDE) strategy. By leveraging the generative capabilities of its Whisper-based structure—just like LLMs—Gladia’s KDE captures context to determine and extract related data straight.
This course of might be additional enhanced with options like customized vocabulary and NER, permitting companies to populate CRMs with key information shortly and effectively.
In your opinion, how is real-time transcription reworking industries equivalent to buyer assist, gross sales, and content material creation?
Actual-time transcription is reshaping these industries in profound methods, driving unbelievable productiveness features, coupled with tangible enterprise advantages.
First, real-time transcription is a game-changer for assist groups. Actual-time help is vital to enhancing the decision charge due to sooner responses, smarter brokers, and higher outcomes (by way of NSF, deal with instances, and so forth). As ASR programs get higher and higher at dealing with non-English languages and performing real-time translation, contact facilities can obtain a very world CX at decrease margins.
In gross sales, pace and spot-on insights are every thing. Equally to what occurs with name brokers, real-time transcription is what equips them with the fitting insights on the proper time, enabling them to concentrate on what issues essentially the most in closing offers.
For creators, real-time transcription is maybe much less related as we speak, however nonetheless filled with potential, particularly relating to dwell captioning and translation throughout media occasions. Most of our present media clients nonetheless choose asynchronous transcription, as pace is much less essential there, whereas accuracy is vital for functions like time-stamped video modifying and subtitle technology.
Actual-time AI transcription appears to be a rising pattern. The place do you see this know-how 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. Basically, what we actually consult with right here is the seamless capability of machines to work together with folks, the way in which we people already work together with each other.
And in case you take a look at any Hollywood film (like Her) set sooner or later, you’ll by no means see anybody there interacting with clever programs by way of a keyboard. For me, that serves as the last 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 principle vector to mixture and share human information, 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 information extra successfully than counting on the neighborhood elders to be the guardians of our tales and knowledge.
GenAI programs, able to understanding speech, producing responses, and storing our interactions, introduced one thing utterly new to the house. It’s the very best of each phrases and the very best 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. Because of this I consider it’s going to be in every single place – it is our final collective dream.
Thanks for the nice interview, readers who want to be taught extra ought to go to Gladia.
