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Amr Nour-Eldin, is the Vice President of Expertise 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 methods for streaming end-to-end speech recognition.
LXT is an rising chief in AI coaching knowledge to energy clever expertise for international organizations. In partnership with a world community of contributors, LXT collects and annotates knowledge throughout a number of modalities with the pace, scale and agility required by the enterprise. Their international experience spans greater than 145 international locations and over 1000 language locales.
You pursued a PhD in Sign Processing from McGill College, what initially you on this discipline?
I all the time wished to review engineering, and actually appreciated pure sciences typically, however was drawn extra particularly to math and physics. I discovered myself all the time making an attempt to determine how nature works and easy 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 proper mixture for my part of each idea 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 for my part, 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 recent 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 way of physics and equations. You are speaking about indicators, whether or not it is audio, pictures or video; understanding how we talk and what our senses understand, and easy methods to mathematically signify that data in a means that permits us to leverage that information to create and enhance expertise.
May 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 discipline academically. After one 12 months of learning Photonics as a part of a Grasp’s diploma in Physics, I made a decision to change 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 discipline a bit of bit into normal audio and speech processing in addition to the closely-related fields of Machine Studying and Data Concept, somewhat than simply specializing in the speech recognition software.
The automobile for my PhD was the bandwidth extension of narrowband speech. Narrowband speech refers to standard telephony speech. The frequency content material of speech extends to round 20 kilohertz, however the majority of the knowledge 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 greater frequency content material given solely the accessible slim band content material, one has to first quantify the mutual data between speech content material within the two frequency bands, then use that data to coach a mannequin that learns that shared data; a mannequin that, as soon as educated, can then be used to generate highband content material given solely narrowband speech and what the mannequin discovered concerning the relationship between that accessible narrowband speech and the lacking highband content material. Quantifying and representing that shared “mutual data” is the place data idea is available in. Data idea is the research of quantifying and representing data in any sign. So my analysis was about incorporating data idea to enhance the factitious bandwidth extension of speech. As such, my PhD was extra of an interdisciplinary analysis exercise the place I mixed sign processing with data idea and machine studying.
You have been a Principal Speech Scientist at Nuance Communications, now part of Microsoft, for over 16 years, what have 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 methods in sign processing and machine studying and making use of that expertise to real-world purposes. I bought the possibility to use these methods to Conversational AI merchandise throughout a number of domains. These domains ranged from enterprise, to healthcare, automotive, and mobility, amongst others. A number of the particular purposes included digital assistants, interactive voice response, voicemail to textual content, and others the place correct illustration and transcription is vital, comparable to 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 way of the gradual takeover of Deep Studying, to now the place deep studying proliferates and dominates nearly all points of AI, together with Generative AI in addition to conventional predictive or discriminative AI. One other key takeaway from that have is the essential function that knowledge performs, by way of amount and high quality, as a key driver of AI mannequin capabilities and efficiency.
You’ve revealed a dozen papers together with in such acclaimed publications as IEEE. In your opinion, what’s the most groundbreaking paper that you just revealed and why was it necessary?
Essentially the most impactful one, by variety of citations in response to Google Scholar, could be a 2008 paper titled “Mel-Frequency Cepstral Coefficient-Based mostly Bandwidth Extension of Narrowband Speech”. At a excessive degree, the main target of this paper is about easy methods to reconstruct speech content material utilizing a characteristic illustration that’s extensively used within the discipline of computerized speech recognition (ASR), mel-frequency cepstral coefficients.
Nonetheless, the extra modern paper for my part, 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 approach that includes temporal data in speech. The benefit of that approach is that it permits modeling long-term data in speech with minimal extra complexity and in a style that also additionally permits the technology of wideband speech in a streaming or real-time style.
In June 2023 you have been recruited as Vice President of Expertise at LXT, what attracted you to this place?
All through my educational {and professional} expertise previous to LXT, I’ve all the time labored immediately with knowledge. The truth is, as I famous earlier, one key takeaway for me from my work with speech science and machine studying was the essential function knowledge performed within the AI mannequin life cycle. Having sufficient high quality knowledge in the appropriate format was, and continues to be, very important to the success of state-of-the-art deep-learning-based AI. As such, once I occurred to be at a stage of my profession the place I used to be looking for a startup-like surroundings the place I may study, broaden my abilities, in addition to leverage my speech and AI expertise to have probably the most affect, I used to be lucky to have the chance to hitch LXT. It was the proper match. Not solely is LXT an AI knowledge supplier that’s rising at a formidable and constant tempo, however I additionally noticed it as on the excellent stage by way of development in AI know-how in addition to in consumer dimension and variety, and therefore in AI and AI knowledge varieties. I relished the chance to hitch and assist in its development journey; to have a big effect by bringing the attitude of a knowledge finish person after having been an AI knowledge scientist person for all these years.
What does your common day at LXT appear to be?
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 will apply that to our prospects’ wants. Fortunately, I’ve a superb group that could be very adept at creating and tailoring options to our shoppers’ often-specialized AI knowledge wants. So, I work intently with them to set that agenda.
There may be additionally, after all, strategic annual and quarterly planning, and breaking down strategic aims into particular person group targets and maintaining up to the mark with developments alongside these plans. As for the characteristic improvement we’re doing, we usually have two expertise tracks. One is to verify we’ve got the appropriate items in place to ship the very best outcomes on our present and new incoming initiatives. The opposite monitor is bettering and increasing our expertise capabilities, with a give attention to incorporating machine studying into them.
May you talk about the forms of machine studying algorithms that you just work on at LXT?
Synthetic intelligence options are remodeling companies throughout all industries, and we at LXT are honored to offer the high-quality knowledge to coach the machine studying algorithms that energy them. Our prospects are engaged on a variety of purposes, together with augmented and digital actuality, pc 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 long run by way of knowledge technology 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 knowledge high quality validation, to enabling a human-in-the-loop labeling mannequin throughout all knowledge modalities we work on.
Speech and audio processing is quickly approaching close to perfection with regards to English and particularly white males. How lengthy do you anticipate it is going to be till it’s a fair enjoying discipline throughout all languages, genders, and ethnicities?
This can be a difficult query, and is dependent upon various components, together with the financial, political, social and technological, amongst others. However what is evident 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 degree enjoying discipline actually is dependent upon the pace at which the illustration of knowledge 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.
Nonetheless, LXT and related corporations can have a giant hand in driving us towards a extra degree enjoying discipline. So long as the info for much less well-represented languages, genders and ethnicities is difficult to entry or just not accessible, that change will come extra slowly. However we try 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 knowledge attainable.
What’s your imaginative and prescient for the way LXT can speed up AI efforts for various shoppers?
Our objective at LXT is to offer the info options that allow environment friendly, correct, and sooner AI improvement. By our 12 years of expertise within the AI knowledge house, not solely have we amassed intensive know-how about shoppers’ wants by way of all points referring to knowledge, however we’ve got additionally constantly fine-tuned our processes with the intention to ship the very best high quality knowledge on the quickest tempo and finest worth factors. Consequently, on account of our steadfast dedication to offering our shoppers the optimum mixture of AI knowledge high quality, effectivity, and pricing, we’ve got develop into a trusted AI knowledge companion as evident by our repeat shoppers who maintain coming again to LXT for his or her ever-growing and evolving AI knowledge wants. My imaginative and prescient is to cement, enhance and develop that LXT “MO” to all of the modalities of knowledge we work on in addition to to all forms of AI improvement we now serve, together with generative AI. Reaching this objective revolves round strategically increasing our personal machine studying and knowledge science capabilities, each by way of expertise in addition to assets.
Thanks for the nice interview, readers who want to study extra ought to go to LXT.
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