Jean-Louis Quéguiner is the Founder and CEO of Gladia. He beforehand served as Group Vice President of Information, 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 varied industries, together with monetary information analytics, machine studying functions 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 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 instances resembling gross sales help and automatic buyer help.
What impressed you to sort out the challenges in speech-to-text (STT) know-how, and what gaps did you see out there?
After I based Gladia, the preliminary purpose was broad—an AI firm that will make advanced know-how accessible. However as we delved deeper, it turned clear that voice know-how was probably the most damaged and but most crucial space to deal with.
Voice is central to our day by day lives, and most of our communication occurs via 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 needed 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 purpose 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 answer.
What are among the distinctive challenges you encountered whereas constructing a transcription answer for enterprise use?
With regards to speech recognition, pace and accuracy—the 2 key efficiency indicators on this area—are inversely proportional by design. Which means that enhancing one will compromise the opposite, at the least to some extent. The associated fee issue, to an enormous extent, outcomes from the supplier’s alternative between pace and high quality.
When constructing Gladia, our purpose was to seek out the right steadiness 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 because of their coaching information, which leaves loads of languages under-represented.
So, along with fixing the speed-accuracy tradeoff, it was vital to us— as a European, multilingual staff—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 aside from English. Our languages help extends to over 100 languages as we speak.
We additionally put a particular emphasis on making the product really 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 facet can convey 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 often 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 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 wrong.
Whereas hallucinations turned identified 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 because of the same structure, so it’s an issue that issues generative fashions, which might be in a position to predict the phrases that observe based mostly on the general context. In a method, they ‘invent’ the output. This method might be contrasted with extra conventional, acoustic-based ASR architectures that match the enter sound to output in a extra mechanical method
Because of this, chances are you’ll discover phrases in a transcript that weren’t truly stated, 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 widespread method is to make use of a retrieval-augmented era (RAG) system, which mixes the mannequin’s generative capabilities with a retrieval mechanism to cross-check info. One other methodology includes using a “chain of thought” method, the place the mannequin is guided via a sequence of predefined steps or checkpoints to make sure that it stays on a logical path.
One other technique for detecting hallucinations includes utilizing techniques 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 mixture of strategies when constructing Whisper-Zero, our proprietary ASR that removes just about all hallucinations. It’s confirmed wonderful leads to asynchronous transcription, and we’re at present 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 method these challenges to make sure excessive accuracy?
Language detection in ASR is a particularly advanced job. Every speaker has a novel 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” area, specializing in how we understand sound. It emphasizes decrease frequencies and makes use of strategies like normalized Fourier decomposition to transform audio right into a frequency spectrum.
Nonetheless, this method has a limitation: it is based mostly purely on acoustics. So, in case you communicate English with a robust accent, the system might not perceive the content material however as a substitute choose based mostly in your prosody (rhythm, stress, intonation).
That is the place Gladia’s revolutionary answer 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 communicate, but in addition 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 vital function in dealing with multilingual conversations. Audio system might swap between languages mid-conversation (and even mid-sentence), and the flexibility of the mannequin to transcribe precisely on the fly regardless of the swap is important.
Gladia API is exclusive in its capacity to deal with code-switching with this many language pairs with a excessive degree of accuracy and performs effectively even in noisy environments, identified to cut 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?
Preserving 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 facility and effectivity of GPGPUs. I’ve been working on this house for practically a decade, main the AI division at OVHCloud (the largest cloud supplier within the EU), and realized firsthand that it’s at all times about discovering the appropriate steadiness: 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 may actually impression 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 helpful milliseconds.
So, it’s actually a mixture of all these elements—good {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 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 here are some examples:
- Good note-taking: Many purchasers are constructing instruments for professionals who must shortly seize and arrange data from work conferences, pupil lectures, or medical consultations. With speaker diarization, our API can determine who stated what, making it straightforward to observe conversations and assign motion objects. Mixed with time-stamped transcripts, customers can leap 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 part. Groups are utilizing our sentiment evaluation function 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 tackle shopper issues 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 middle help: Corporations within the contract middle house are utilizing our API to offer reside help to brokers, in addition to flagging buyer sentiment throughout calls. Speaker diarization ensures that issues being stated are assigned to the appropriate particular person, whereas time-stamped transcripts allow supervisors to evaluation important moments or compliance points shortly. This not solely improves the client expertise – with higher on-call decision fee and high quality monitoring – but in addition 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 answer to adapt to those particular wants, which is essential for capturing contextual nuances and delivering output that precisely displays what you are promoting wants. As an illustration, it lets you create an inventory of domain-specific phrases, resembling model names, in a particular language.
Why it’s helpful: Adapting the transcription to the precise vertical lets you reduce errors in transcripts, reaching a greater consumer expertise. This function is very important in fields like drugs or finance.
Named entity recognition (NER) extracts and identifies key data from unstructured audio information, resembling names of individuals, organizations, areas, and extra. A standard problem with unstructured information is that this important data isn’t readily accessible—it is buried inside the transcript.
To resolve this, Gladia developed a structured Key Information Extraction (KDE) method. 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 remodeling industries resembling buyer help, gross sales, and content material creation?
Actual-time transcription is reshaping these industries in profound methods, driving unbelievable productiveness positive aspects, 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 fee because of sooner responses, smarter brokers, and higher outcomes (by way of NSF, deal with occasions, and so forth). As ASR techniques 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 part. Equally to what occurs with name brokers, real-time transcription is what equips them with the appropriate insights on the proper time, enabling them to deal with what issues probably the most in closing offers.
For creators, real-time transcription is probably much less related as we speak, however nonetheless filled with potential, particularly on the subject of reside captioning and translation throughout media occasions. Most of our present media clients nonetheless want asynchronous transcription, as pace is much less important there, whereas accuracy is essential for functions like time-stamped video enhancing and subtitle era.
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 all places. Primarily, what we actually seek advice from right here is the seamless capacity of machines to work together with individuals, 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 techniques through a keyboard. For me, that serves as the last word proof that within the collective creativeness of humanity, voice will at all times be the first method we work together with the world round us.
Voice, as the primary 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 techniques, able to understanding speech, producing responses, and storing our interactions, introduced one thing fully new to the house. It’s one of the best of each phrases and one of the best of humanity actually. It offers us this distinctive energy and vitality of voice communication with the advantage of reminiscence, which beforehand solely written media might safe for us. For this reason I consider it’s going to be in all places – it is our final collective dream.
Thanks for the good interview, readers who want to study extra ought to go to Gladia.
