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Building voice AI that listens to everyone: Transfer learning and synthetic speech in action

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Have you ever thought about what it is like to use a voice assistant when your own voice does not match what the system expects? AI is not just reshaping how we hear the world; it is transforming who gets to be heard. In the age of conversational AI, accessibility has become a crucial benchmark for innovation. Voice assistants, transcription tools and audio-enabled interfaces are everywhere. One downside is that for millions of people with speech disabilities, these systems can often fall short.

As someone who has worked extensively on speech and voice interfaces across automotive, consumer and mobile platforms, I have seen the promise of AI in enhancing how we communicate. In my experience leading development of hands-free calling, beamforming arrays and wake-word systems, I have often asked: What happens when a user’s voice falls outside the model’s comfort zone? That question has pushed me to think about inclusion not just as a feature but a responsibility.

In this article, we will explore a new frontier: AI that can not only enhance voice clarity and performance, but fundamentally enable conversation for those who have been left behind by traditional voice technology.

Rethinking conversational AI for accessibility

To better understand how inclusive AI speech systems work, let us consider a high-level architecture that begins with nonstandard speech data and leverages transfer learning to fine-tune models. These models are designed specifically for atypical speech patterns, producing both recognized text and even synthetic voice outputs tailored for the user.

Standard speech recognition systems struggle when faced with atypical speech patterns. Whether due to cerebral palsy, ALS, stuttering or vocal trauma, people with speech impairments are often misheard or ignored by current systems. But deep learning is helping change that. By training models on nonstandard speech data and applying transfer learning techniques, conversational AI systems can begin to understand a wider range of voices.

Beyond recognition, generative AI is now being used to create synthetic voices based on small samples from users with speech disabilities. This allows users to train their own voice avatar, enabling more natural communication in digital spaces and preserving personal vocal identity.

There are even platforms being developed where individuals can contribute their speech patterns, helping to expand public datasets and improve future inclusivity. These crowdsourced datasets could become critical assets for making AI systems truly universal.

Assistive features in action

Real-time assistive voice augmentation systems follow a layered flow. Starting with speech input that may be disfluent or delayed, AI modules apply enhancement techniques, emotional inference and contextual modulation before producing clear, expressive synthetic speech. These systems help users speak not only intelligibly but meaningfully.

Have you ever imagined what it would feel like to speak fluidly with assistance from AI, even if your speech is impaired? Real-time voice augmentation is one such feature making strides. By enhancing articulation, filling in pauses or smoothing out disfluencies, AI acts like a co-pilot in conversation, helping users maintain control while improving intelligibility. For individuals using text-to-speech interfaces, conversational AI can now offer dynamic responses, sentiment-based phrasing, and prosody that matches user intent, bringing personality back to computer-mediated communication.

Another promising area is predictive language modeling. Systems can learn a user’s unique phrasing or vocabulary tendencies, improve predictive text and speed up interaction. Paired with accessible interfaces such as eye-tracking keyboards or sip-and-puff controls, these models create a responsive and fluent conversation flow.

Some developers are even integrating facial expression analysis to add more contextual understanding when speech is difficult. By combining multimodal input streams, AI systems can create a more nuanced and effective response pattern tailored to each individual’s mode of communication.

A personal glimpse: Voice beyond acoustics

I once helped evaluate a prototype that synthesized speech from residual vocalizations of a user with late-stage ALS. Despite limited physical ability, the system adapted to her breathy phonations and reconstructed full-sentence speech with tone and emotion. Seeing her light up when she heard her “voice” speak again was a humbling reminder: AI is not just about performance metrics. It is about human dignity.

I have worked on systems where emotional nuance was the last challenge to overcome. For people who rely on assistive technologies, being understood is important, but feeling understood is transformational. Conversational AI that adapts to emotions can help make this leap.

Implications for builders of conversational AI

For those designing the next generation of virtual assistants and voice-first platforms, accessibility should be built-in, not bolted on. This means collecting diverse training data, supporting non-verbal inputs, and using federated learning to preserve privacy while continuously improving models. It also means investing in low-latency edge processing, so users do not face delays that disrupt the natural rhythm of dialogue.

Enterprises adopting AI-powered interfaces must consider not only usability, but inclusion. Supporting users with disabilities is not just ethical, it is a market opportunity. According to the World Health Organization, more than 1 billion people live with some form of disability. Accessible AI benefits everyone, from aging populations to multilingual users to those temporarily impaired.

Additionally, there is a growing interest in explainable AI tools that help users understand how their input is processed. Transparency can build trust, especially among users with disabilities who rely on AI as a communication bridge.

Looking forward

The promise of conversational AI is not just to understand speech, it is to understand people. For too long, voice technology has worked best for those who speak clearly, quickly and within a narrow acoustic range. With AI, we have the tools to build systems that listen more broadly and respond more compassionately.

If we want the future of conversation to be truly intelligent, it must also be inclusive. And that starts with every voice in mind.

Harshal Shah is a voice technology specialist passionate about bridging human expression and machine understanding through inclusive voice solutions.



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Dozens die in Vietnam as tourist ferry sinks in Ha Long Bay

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Rescuers were desperately searching for five people still missing on Sunday after 37 were killed when a boat capsized in one of Vietnam’s most popular tourist destinations.

The tourist boat ferrying families around Vietnam’s famed Ha Long Bay was lashed by a storm on Saturday in one of the UNESCO World Heritage site’s deadliest disasters.

The “Wonder Sea” vessel was carrying 48 passengers and five crew members when it capsized because of sudden heavy rain, the VNExpress news site said.

Most of those on board were families visiting from the capital Hanoi, with more than 20 children among the passengers, it said.

Border guards had rescued 11 people and recovered 34 bodies by Saturday evening, it added.

Overnight three crew members’ bodies were found in the cabin and rescue efforts continued into Sunday morning to find the five people still missing.

One of the rescued, a 10-year-old boy, told state media outlet VietnamNet: “I took a deep breath, swam through a gap, dived then swam up, I even shouted for help, then I was pulled up by a boat with soldiers on”.

Prime Minister Pham Minh Chinh sent his condolences on Saturday to the families of the deceased and called on the defence and public security ministries to conduct urgent search and rescue.

Authorities would “investigate and clarify the cause of the incident and strictly handle violations”, a government statement said.

Tran Trong Hung, a resident in the Ha Long Bay area, told AFP: “The sky turned dark at around 2:00 pm.”

There were “hailstones as big as toes with torrential rain, thunderstorm and lightning”, he said.

Torrential rain also lashed northern Hanoi, Thai Nguyen and Bac Ninh provinces on Saturday.

Several trees were knocked down in the capital by strong winds.

The storm followed three days of intense heat, with the mercury hitting 37 degrees Celsius (99 degrees Fahrenheit) in some areas.

Mai Van Khiem, director of the National Center for Hydrometeorological Forecasting, was quoted in VNExpress as saying that the thunderstorms in northern Vietnam were not caused by the influence of Tropical Storm Wipha in the South China Sea. Wipha entered the South China Sea on Sunday gaining strength, and is on course to make landfall in Vietnam early next week. Ha Long Bay is one of Vietnam’s most popular tourist destinations, with millions of people visiting its blue-green waters and rainforest-topped limestone islands each year. Last year, 30 vessels sank at boat lock areas in coastal Quang Ninh province along Ha Long Bay after Typhoon Yagi brought strong wind and waves. And this month, a ferry sank off the popular Indonesian resort island of Bali, killing at least 18 people.



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Weaving reality or warping it? The personalization trap in AI systems

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AI represents the greatest cognitive offloading in the history of humanity. We once offloaded memory to writing, arithmetic to calculators and navigation to GPS. Now we are beginning to offload judgment, synthesis and even meaning-making to systems that speak our language, learn our habits and tailor our truths.

AI systems are growing increasingly adept at recognizing our preferences, our biases, even our peccadillos. Like attentive servants in one instance or subtle manipulators in another, they tailor their responses to please, to persuade, to assist or simply to hold our attention. 

While the immediate effects may seem benign, in this quiet and invisible tuning lies a profound shift: The version of reality each of us receives becomes progressively more uniquely tailored. Through this process, over time, each person becomes increasingly their own island. This divergence could threaten the coherence and stability of society itself, eroding our ability to agree on basic facts or navigate shared challenges.

AI personalization does not merely serve our needs; it begins to reshape them. The result of this reshaping is a kind of epistemic drift. Each person starts to move, inch by inch, away from the common ground of shared knowledge, shared stories and shared facts, and further into their own reality. 


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This is not simply a matter of different news feeds. It is the slow divergence of moral, political and interpersonal realities. In this way, we may be witnessing the unweaving of collective understanding. It is an unintended consequence, yet deeply significant precisely because it is unforeseen. But this fragmentation, while now accelerated by AI, began long before algorithms shaped our feeds.

The unweaving

This unweaving did not begin with AI. As David Brooks reflected in The Atlantic, drawing on the work of philosopher Alasdair MacIntyre, our society has been drifting away from shared moral and epistemic frameworks for centuries. Since the Enlightenment, we have gradually replaced inherited roles, communal narratives and shared ethical traditions with individual autonomy and personal preference. 

What began as liberation from imposed belief systems has, over time, eroded the very structures that once tethered us to common purpose and personal meaning. AI did not create this fragmentation. But it is giving new form and speed to it, customizing not only what we see but how we interpret and believe.

It is not unlike the biblical story of Babel. A unified humanity once shared a single language, only to be fractured, confused and scattered by an act that made mutual understanding all but impossible. Today, we are not building a tower made of stone. We are building a tower of language itself. Once again, we risk the fall.

Human-machine bond

At first, personalization was a way to improve “stickiness” by keeping users engaged longer, returning more often and interacting more deeply with a site or service. Recommendation engines, tailored ads and curated feeds were all designed to keep our attention just a little longer, perhaps to entertain but often to move us to purchase a product. But over time, the goal has expanded. Personalization is no longer just about what holds us. It is what it knows about each of us, the dynamic graph of our preferences, beliefs and behaviors that becomes more refined with every interaction.

Today’s AI systems do not merely predict our preferences. They aim to create a bond through highly personalized interactions and responses, creating a sense that the AI system understands and cares about the user and supports their uniqueness. The tone of a chatbot, the pacing of a reply and the emotional valence of a suggestion are calibrated not only for efficiency but for resonance, pointing toward a more helpful era of technology. It should not be surprising that some people have even fallen in love and married their bots

The machine adapts not just to what we click on, but to who we appear to be. It reflects us back to ourselves in ways that feel intimate, even empathic. A recent research paper cited in Nature refers to this as “socioaffective alignment,” the process by which an AI system participates in a co-created social and psychological ecosystem, where preferences and perceptions evolve through mutual influence.

This is not a neutral development. When every interaction is tuned to flatter or affirm, when systems mirror us too well, they blur the line between what resonates and what is real. We are not just staying longer on the platform; we are forming a relationship. We are slowly and perhaps inexorably merging with an AI-mediated version of reality, one that is increasingly shaped by invisible decisions about what we are meant to believe, want or trust. 

This process is not science fiction; its architecture is built on attention, reinforcement learning with human feedback (RLHF) and personalization engines. It is also happening without many of us — likely most of us — even knowing. In the process, we gain AI “friends,” but at what cost? What do we lose, especially in terms of free will and agency?

Author and financial commentator Kyla Scanlon spoke on the Ezra Klein podcast about how the frictionless ease of the digital world may come at the cost of meaning. As she put it: “When things are a little too easy, it’s tough to find meaning in it… If you’re able to lay back, watch a screen in your little chair and have smoothies delivered to you — it’s tough to find meaning within that kind of WALL-E lifestyle because everything is just a bit too simple.”

The personalization of truth

As AI systems respond to us with ever greater fluency, they also move toward increasing selectivity. Two users asking the same question today might receive similar answers, differentiated mostly by the probabilistic nature of generative AI. Yet this is merely the beginning. Emerging AI systems are explicitly designed to adapt their responses to individual patterns, gradually tailoring answers, tone and even conclusions to resonate most strongly with each user. 

Personalization is not inherently manipulative. But it becomes risky when it is invisible, unaccountable or engineered more to persuade than to inform. In such cases, it does not just reflect who we are; it steers how we interpret the world around us.

As the Stanford Center for Research on Foundation Models notes in its 2024 transparency index, few leading models disclose whether their outputs vary by user identity, history or demographics, although the technical scaffolding for such personalization is increasingly in place and only beginning to be examined. While not yet fully realized across public platforms, this potential to shape responses based on inferred user profiles, resulting in increasingly tailored informational worlds, represents a profound shift that is already being prototyped and actively pursued by leading companies.

This personalization can be beneficial, and certainly that is the hope of those building these systems. Personalized tutoring shows promise in helping learners progress at their own pace. Mental health apps increasingly tailor responses to support individual needs, and accessibility tools adjust content to meet a range of cognitive and sensory differences. These are real gains. 

But if similar adaptive methods become widespread across information, entertainment and communication platforms, a deeper, more troubling shift looms ahead: A transformation from shared understanding toward tailored, individual realities. When truth itself begins to adapt to the observer, it becomes fragile and increasingly fungible. Instead of disagreements based primarily on differing values or interpretations, we could soon find ourselves struggling simply to inhabit the same factual world.

Of course, truth has always been mediated. In earlier eras, it passed through the hands of clergy, academics, publishers and evening news anchors who served as gatekeepers, shaping public understanding through institutional lenses. These figures were certainly not free from bias or agenda, yet they operated within broadly shared frameworks.

Today’s emerging paradigm promises something qualitatively different: AI-mediated truth through personalized inference that frames, filters and presents information, shaping what users come to believe. But unlike past mediators who, despite flaws, operated within publicly visible institutions, these new arbiters are commercially opaque, unelected and constantly adapting, often without disclosure. Their biases are not doctrinal but encoded through training data, architecture and unexamined developer incentives.

The shift is profound, from a common narrative filtered through authoritative institutions to potentially fractured narratives that reflect a new infrastructure of understanding, tailored by algorithms to the preferences, habits and inferred beliefs of each user. If Babel represented the collapse of a shared language, we may now stand at the threshold of the collapse of shared mediation.

If personalization is the new epistemic substrate, what might truth infrastructure look like in a world without fixed mediators? One possibility is the creation of AI public trusts, inspired by a proposal from legal scholar Jack Balkin, who argued that entities handling user data and shaping perception should be held to fiduciary standards of loyalty, care and transparency. 

AI models could be governed by transparency boards, trained on publicly funded data sets and required to show reasoning steps, alternate perspectives or confidence levels. These “information fiduciaries” would not eliminate bias, but they could anchor trust in process rather than purely in personalization. Builders can begin by adopting transparent “constitutions” that clearly define model behavior, and by offering chain-of-reasoning explanations that let users see how conclusions are shaped. These are not silver bullets, but they are tools that help keep epistemic authority accountable and traceable.

AI builders face a strategic and civic inflection point. They are not just optimizing performance; they are also confronting the risk that personalized optimization may fragment shared reality. This demands a new kind of responsibility to users: Designing systems that respect not only their preferences, but their role as learners and believers.

Unraveling and reweaving

What we may be losing is not simply the concept of truth, but the path through which we once recognized it. In the past, mediated truth — although imperfect and biased — was still anchored in human judgment and, often, only a layer or two removed from the lived experience of other humans whom you knew or could at least relate to. 

Today, that mediation is opaque and driven by algorithmic logic. And, while human agency has long been slipping, we now risk something deeper, the loss of the compass that once told us when we were off course. The danger is not only that we will believe what the machine tells us. It is that we will forget how we once discovered the truth for ourselves. What we risk losing is not just coherence, but the will to seek it. And with that, a deeper loss: The habits of discernment, disagreement and deliberation that once held pluralistic societies together. 

If Babel marked the shattering of a common tongue, our moment risks the quiet fading of shared reality. However, there are ways to slow or even to counter the drift. A model that explains its reasoning or reveals the boundaries of its design may do more than clarify output. It may help restore the conditions for shared inquiry. This is not a technical fix; it is a cultural stance. Truth, after all, has always depended not just on answers, but on how we arrive at them together. 



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Tourists warned ‘never wear’ these two items at the airport or face delays

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Travel experts have shared that there are two items that you should never wear at the airport as they can lead to delays. Here’s everything you need to know.

Holidaymakers should be careful about travelling in certain garments which could cause delay(Image: Getty Images)

Many of us are right in the swing of booking exciting getaways as the season is underway. Tourists are flocking to airports in droves at some of the busiest months for some much needed sun and R&R.

However holidaymakers who are preparing to jet off soon have been warned that certain items may cause unnecessary hold-ups when in the airport, which could be an oversight. Many of us choose comfort when travelling, but you also want to ensure it’s hassle free.

Jetpac‘s travel specialist Pearlyn Yeo, boasting a substantial Instagram following of over 20,000 on the company platform, has shared tips on what to avoid wearing at the airport.

Following the expert guidance, as outlined in the Mirror, could dramatically enhance your jet-setting this year.

The specialist revealed: “Keeping sunglasses on or wearing headphones through passport control can cause unnecessary delays and stress. These accessories can make it harder for border agents and automated systems to confirm your identity or get your attention.

Multiracial group of passengers passing by airport security check.
There are two items you may want to avoid wearing(Image: izusek via Getty Images)

“Both facial recognition gates and human officers rely on clear eye contact and unobstructed facial features. Sunglasses can interfere with the technology and an officer’s ability to assess your behaviour and demeanour.”

He noted: “Headphones, on the other hand, can distract you and mean you miss important instructions – both of which can slow down the process. As well as removing the above accessories before heading through security and passport control, it’s important that travellers are prepared.

“At Jetpac, we advise all travellers to keep their documents digitally and ensure they are easy to access. eSIMs can help to organise everything you need. But it’s also wise to know when to put your devices and any other distractions away.

“For example, when going through immigration. Removing accessories and putting devices away shows that you’re ready and also that you respect certain protocols.”

The expert continued: “Most security staff will ask you to remove these items or stop using devices, so it will only add to your journey time and stress if you’re not prepared.

“With this in mind, it’s best to remove headphones, pack away phones and tablets and pop your sunglasses safely away before you reach security, passport control or immigration.

“In doing so, you’re more likely to breeze through border control and you won’t have to worry about unnecessary delays or stress.”

When choosing travel clothes, there are several key rules that should always be followed. The main is choosing something which is practical, by wearing layered clothing and breathable materials, and of course comfortable footwear.

It’s also sensible to avoid tight-fitting garments, bulky items and anything with an excess of metal that could potentially delay security checks. By adhering to these guidelines, you’re likely to sail through security checks.

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