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 Automated Speech Recognition (ASR), with a specific focus and hands-on expertise in recent times on deep studying strategies for streaming end-to-end speech recognition.
LXT is an rising chief in AI coaching knowledge to energy clever expertise for world 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 world experience spans greater than 115 nations and over 780 language locales.
You pursued a PhD in Sign Processing from McGill College, what initially you on this area?
I all the time needed to check engineering, and actually favored 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 how you can apply that understanding to create expertise. After highschool, I had the chance to enter drugs and different professions, however particularly selected engineering because it represented the proper mixture for my part of each principle and utility 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 typically 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 via 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 how you can mathematically symbolize that data in a approach 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 facet of synthetic Bandwidth extension (BWE)?
After I completed my bachelor’s diploma, I needed to maintain pursuing the Sign Processing area academically. After one 12 months of learning 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 needed to broaden my area just a little bit into basic audio and speech processing in addition to the closely-related fields of Machine Studying and Data Idea, slightly than simply specializing in the speech recognition utility.
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 certain in standard telephony, to above that, as much as eight kilohertz or extra. To higher reconstruct that lacking greater frequency content material given solely the accessible slender 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 principle is available in. Data principle is the examine of quantifying and representing data in any sign. So my analysis was about incorporating data principle to enhance the bogus bandwidth extension of speech. As such, my PhD was extra of an interdisciplinary analysis exercise the place I mixed sign processing with data principle 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, crucial 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 bought the prospect 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 crucial, reminiscent of 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, via the gradual takeover of Deep Studying, to now the place deep studying proliferates and dominates virtually 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 position that knowledge performs, via 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 simply printed and why was it essential?
Probably the most impactful one, by variety of citations in line with Google Scholar, can be a 2008 paper titled “Mel-Frequency Cepstral Coefficient-Primarily based Bandwidth Extension of Narrowband Speech”. At a excessive degree, the main focus of this paper is about how you can reconstruct speech content material utilizing a characteristic illustration that’s broadly used within the area 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-Primarily based 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 comes with 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 trend that also additionally permits the era of wideband speech in a streaming or real-time trend.
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 immediately with knowledge. In reality, as I famous earlier, one key takeaway for me from my work with speech science and machine studying was the essential position 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 searching for a startup-like atmosphere the place I may be taught, broaden my abilities, in addition to leverage my speech and AI expertise to have essentially the most affect, I used to be lucky to have the chance to affix LXT. It was the proper match. Not solely is LXT an AI knowledge supplier that’s rising at a powerful and constant tempo, however I additionally noticed it as on the good stage by way of development in AI know-how in addition to in shopper measurement and variety, and therefore in AI and AI knowledge sorts. I relished the chance to affix and assist in its development journey; to have a big effect by bringing the attitude of an information finish consumer after having been an AI knowledge scientist consumer 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 these days 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 purchasers’ often-specialized AI knowledge wants. So, I work intently with them to set that agenda.
There may be additionally, in fact, strategic annual and quarterly planning, and breaking down strategic targets into particular person workforce targets and conserving on top of things with developments alongside these plans. As for the characteristic growth we’re doing, we typically have two expertise tracks. One is to verify we’ve the appropriate items in place to ship the perfect outcomes on our present and new incoming initiatives. The opposite monitor is enhancing and increasing our expertise capabilities, with a concentrate on incorporating machine studying into them.
May you talk about the sorts of machine studying algorithms that you simply work on at LXT?
Synthetic intelligence options are reworking 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 clients are engaged on a variety of functions, 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 longer term via knowledge 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 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 relating to English and particularly white males. How lengthy do you anticipate will probably be till it’s an excellent taking part in area throughout all languages, genders, and ethnicities?
This can be a difficult query, and relies on quite a few 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 degree taking part in area actually relies on the pace 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 after we get there.
Nonetheless, LXT and related corporations can have an enormous hand in driving us towards a extra degree taking part in area. 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 nations, LXT helps to make entry to extra language knowledge doable.
What’s your imaginative and prescient for a way LXT can speed up AI efforts for various purchasers?
Our aim at LXT is to offer the info options that allow environment friendly, correct, and quicker AI growth. By way of our 12 years of expertise within the AI knowledge house, not solely have we collected intensive know-how about purchasers’ wants by way of all points referring to knowledge, however we’ve additionally repeatedly fine-tuned our processes so as to ship the best high quality knowledge on the quickest tempo and finest worth factors. Consequently, because of our steadfast dedication to offering our purchasers the optimum mixture of AI knowledge high quality, effectivity, and pricing, we’ve change into a trusted AI knowledge companion as evident by our repeat purchasers who preserve 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 information we work on in addition to to all sorts of AI growth we now serve, together with generative AI. Reaching this aim revolves round strategically increasing our personal machine studying and knowledge science capabilities, each by way of expertise in addition to sources.
Thanks for the nice interview, readers who want to be taught extra ought to go to LXT.
