Monday, December 11, 2023

Amr Nour-Eldin, Vice President of Know-how at LXT – Interview Collection


Amr Nour-Eldin, is the Vice President of Know-how at LXT. Amr is a Ph.D. analysis scientist with over 16 years {of professional} expertise within the fields of speech/audio processing and machine studying within the context of Computerized Speech Recognition (ASR), with a specific focus and hands-on expertise lately on deep studying strategies for streaming end-to-end speech recognition.

LXT is an rising chief in AI coaching information to energy clever expertise for international organizations. In partnership with a global community of contributors, LXT collects and annotates information throughout a number of modalities with the velocity, scale and agility required by the enterprise. Their international experience spans greater than 115 international locations and over 780 language locales.

You pursued a PhD in Sign Processing from McGill College, what initially you on this subject?

I all the time wished to review engineering, and actually favored pure sciences generally, however was drawn extra particularly to math and physics. I discovered myself all the time making an attempt to determine how nature works and methods to apply that understanding to create expertise. After highschool, I had the chance to enter medication and different professions, however particularly selected engineering because it represented the right mixture in my opinion of each concept and software within the two fields closest to my coronary heart: math and physics. After which as soon as I had chosen it, there have been many potential paths – mechanical, civil, and so forth. However I particularly selected electrical engineering as a result of it is the closest, and the hardest in my opinion, to the kind of math and physics issues which I all the time discovered difficult and therefore, loved extra, in addition to being the muse of contemporary expertise which has all the time pushed me.

Inside electrical engineering, there are numerous specializations to select from, which usually fall below two umbrellas: telecommunications and sign processing, and that of energy and electrical engineering. When the time got here to decide on between these two, I selected telecom and sign processing as a result of it is nearer to how we describe nature by physics and equations. You are speaking about indicators, whether or not it is audio, photographs or video; understanding how we talk and what our senses understand, and methods to mathematically symbolize that info in a manner that enables us to leverage that data to create and enhance expertise.

Might you talk about your analysis at McGill College on the information-theoretic side of synthetic Bandwidth extension (BWE)?

After I completed my bachelor’s diploma, I wished to maintain pursuing the Sign Processing subject academically. After one yr of finding out Photonics as a part of a Grasp’s diploma in Physics, I made a decision to modify again to Engineering to pursue my grasp’s in Audio and Speech sign processing, specializing in speech recognition. When it got here time to do my PhD, I wished to broaden my subject just a little bit into basic audio and speech processing in addition to the closely-related fields of Machine Studying and Data Principle, fairly than simply specializing in the speech recognition software.

The automobile for my PhD was the bandwidth extension of narrowband speech. Narrowband speech refers to traditional telephony speech. The frequency content material of speech extends to round 20 kilohertz, however the majority of the data content material is concentrated as much as simply 4 kilohertz. Bandwidth extension refers to artificially extending speech content material from 3.4 kilohertz, which is the higher frequency sure in standard telephony, to above that, as much as eight kilohertz or extra. To raised reconstruct that lacking larger frequency content material given solely the obtainable slim band content material, one has to first quantify the mutual info between speech content material within the two frequency bands, then use that info to coach a mannequin that learns that shared info; a mannequin that, as soon as educated, can then be used to generate highband content material given solely narrowband speech and what the mannequin realized in regards to the relationship between that obtainable narrowband speech and the lacking highband content material. Quantifying and representing that shared “mutual info” is the place info concept is available in. Data concept is the examine of quantifying and representing info in any sign. So my analysis was about incorporating info concept to enhance the synthetic bandwidth extension of speech. As such, my PhD was extra of an interdisciplinary analysis exercise the place I mixed sign processing with info concept and machine studying.

You had been a Principal Speech Scientist at Nuance Communications, now part of Microsoft, for over 16 years, what had been a few of your key takeaways from this expertise?

From my perspective, an important profit was that I used to be all the time engaged on state-of-the-art, cutting-edge strategies in sign processing and machine studying and making use of that expertise to real-world functions. I received the possibility to use these strategies to Conversational AI merchandise throughout a number of domains. These domains ranged from enterprise, to healthcare, automotive, and mobility, amongst others. Among the particular functions included digital assistants, interactive voice response, voicemail to textual content, and others the place correct illustration and transcription is essential, resembling in healthcare with physician/affected person interactions. All through these 16 years, I used to be lucky to witness firsthand and be a part of the evolution of conversational AI, from the times of statistical modeling utilizing Hidden Markov Fashions, by the gradual takeover of Deep Studying, to now the place deep studying proliferates and dominates virtually all facets of AI, together with Generative AI in addition to conventional predictive or discriminative AI. One other key takeaway from that have is the essential position that information performs, by amount and high quality, as a key driver of AI mannequin capabilities and efficiency.

You’ve printed a dozen papers together with in such acclaimed publications as IEEE. In your opinion, what’s the most groundbreaking paper that you just printed and why was it necessary?

Essentially the most impactful one, by variety of citations in keeping with Google Scholar, could be a 2008 paper titled “Mel-Frequency Cepstral Coefficient-Based mostly Bandwidth Extension of Narrowband Speech”. At a excessive stage, the main focus of this paper  is about methods to reconstruct speech content material utilizing a function illustration that’s broadly used within the subject of computerized speech recognition (ASR), mel-frequency cepstral coefficients.

Nevertheless, the extra progressive paper in my opinion, is a paper with the second-most citations, a 2011 paper titled “Reminiscence-Based mostly Approximation of the Gaussian Combination Mannequin Framework for Bandwidth Extension of Narrowband Speech“. In that work, I proposed a brand new statistical modeling method that includes temporal info in speech. The benefit of that method is that it permits modeling long-term info in speech with minimal further complexity and in a style that also additionally permits the era of wideband speech in a streaming or real-time style.

In June 2023 you had been recruited as Vice President of Know-how at LXT, what attracted you to this place?

All through my educational {and professional} expertise previous to LXT, I’ve all the time labored instantly with information. In actual fact, as I famous earlier, one key takeaway for me from my work with speech science and machine studying was the essential position information performed within the AI mannequin life cycle. Having sufficient high quality information in the fitting format was, and continues to be, important to the success of state-of-the-art deep-learning-based AI. As such, after I occurred to be at a stage of my profession the place I used to be searching for a startup-like atmosphere the place I might be taught, broaden my expertise, in addition to leverage my speech and AI expertise to have probably the most impression, I used to be lucky to have the chance to hitch LXT. It was the right match. Not solely is LXT an AI information supplier that’s rising at a powerful and constant tempo, however I additionally noticed it as on the excellent stage by way of progress in AI know-how in addition to in shopper dimension and variety, and therefore in AI and AI information varieties. I relished the chance to hitch and assist in its progress journey; to have a huge impact by bringing the attitude of an information finish person after having been an AI information scientist person for all these years.

What does your common day at LXT seem like?

My common day begins with trying into the newest analysis on one matter or one other, which has recently centered round generative AI, and the way we are able to apply that to our clients’ wants. Fortunately, I’ve a superb workforce that may be very adept at creating and tailoring options to our shoppers’ often-specialized AI information wants. So, I work carefully with them to set that agenda.

There’s additionally, in fact, strategic annual and quarterly planning, and breaking down strategic aims into particular person workforce objectives and preserving on top of things with developments alongside these plans. As for the function improvement we’re doing, we usually have two expertise tracks. One is to verify we have now the fitting items in place to ship the most effective outcomes on our present and new incoming tasks. The opposite monitor is bettering and increasing our expertise capabilities, with a deal with incorporating machine studying into them.

Might you talk about the forms of machine studying algorithms that you just work on at LXT?

Synthetic intelligence options are reworking companies throughout all industries, and we at LXT are honored to supply the high-quality information to coach the machine studying algorithms that energy them. Our clients are engaged on a variety of functions, together with augmented and digital actuality, laptop imaginative and prescient, conversational AI, generative AI, search relevance and speech and pure language processing (NLP), amongst others. We’re devoted to powering the machine studying algorithms and applied sciences of the longer term by information era and enhancement throughout each language, tradition and modality.

Internally, we’re additionally incorporating machine studying to enhance and optimize our inside processes, starting from automating our information high quality validation, to enabling a human-in-the-loop labeling mannequin throughout all information modalities we work on.

Speech and audio processing is quickly approaching close to perfection relating to English and particularly white males. How lengthy do you anticipate will probably be till it’s a fair taking part in subject throughout all languages, genders, and ethnicities?

It is a difficult query, and is dependent upon quite a lot of elements, together with the financial, political, social and technological, amongst others. However what is obvious is that the prevalence of the English language is what drove AI to the place we at the moment are. So to get to a spot the place it is a stage taking part in subject actually is dependent upon the velocity at which the illustration of information from totally different ethnicities and populations grows on-line, and the tempo at which it grows is what is going to decide once we get there.

Nevertheless, LXT and comparable corporations can have a giant hand in driving us towards a extra stage taking part in subject. So long as the information for much less well-represented languages, genders and ethnicities is difficult to entry or just not obtainable, that change will come extra slowly. However we are attempting to do our half. With protection for over 1,000 language locales and expertise in 145 international locations, LXT helps to make entry to extra language information doable.

What’s your imaginative and prescient for a way LXT can speed up AI efforts for various shoppers?

Our purpose at LXT is to supply the information options that allow environment friendly, correct, and sooner AI improvement. By way of our 12 years of expertise within the AI information area, not solely have we gathered in depth know-how about shoppers’ wants by way of all facets regarding information, however we have now additionally constantly fine-tuned our processes with a view to ship the best high quality information on the quickest tempo and finest value factors. Consequently, because of our steadfast dedication to offering our shoppers the optimum mixture of AI information high quality, effectivity, and pricing, we have now turn into a trusted AI information accomplice as evident by our repeat shoppers who maintain coming again to LXT for his or her ever-growing and evolving AI information wants. My imaginative and prescient is to cement, enhance and increase that LXT “MO” to all of the modalities of information we work on in addition to to all forms of AI improvement we now serve, together with generative AI. Attaining this purpose revolves round strategically increasing our personal machine studying and information science capabilities, each by way of expertise in addition to sources.

Thanks for the good interview, readers who want to be taught extra ought to go to LXT.


Related Articles

Latest Articles