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When we think of artificial intelligence applied to telephone conversations, we often imagine a technology so precise that nothing escapes it. It is tempting to believe that automatic speech transcription is already a “solved problem.” However, behind every virtual assistant answering the phone, there is a technical and human race to decipher language under unpredictable conditions. The reality is that, even today, faithful real-time transcription of a call remains one of the great challenges for conversational AI. And it is so precisely because there is nothing more human – and at the same time more difficult to model – than live voice.

The complexity of capturing the human voice

Transcribing natural language in a telephone environment is, in essence, trying to capture all the richness, ambiguity, and variability of human communication using machines that, by definition, do not share our experience. The voice is not just a set of sounds: it encompasses accent, emotion, context, noise, and, above all, intention. Each call is unique, and for AI, each conversation is a small amalgamation of uncertainties: low-quality lines, overlaps, sudden silences, idiomatic expressions, and abrupt topic changes. If written language already contains nuances that are difficult to model, spoken language multiplies the difficulty. Words blend into each other, pauses do not always delineate the boundaries of meaning and, to complicate things further, humans are experts at interrupting, correcting, or switching languages without warning.

Technical challenges in automatic transcription

The task of transcribing in real time is not just about converting audio waves into text. It involves facing a series of technical challenges that, far from being solved, worsen in the context of phone calls:

• Noise and variable quality: Systems must distinguish useful voice amidst a sea of interferences, echoes, cuts, and distortions. It is not uncommon for the phone channel itself to degrade the signal to the point where even humans have to ask for repetitions.

• Variability of speech: Each person has their accent, rhythm, timbre, speed, and fillers. In countries and cities, the phonetic differences can be so great that, for AI, it is almost like dealing with another language.

• Limited context: In streaming, the AI transcribes on the fly. It cannot wait to hear the entire sentence to understand a key nuance, as a human would while rereading a sentence. This forces quick decision-making, with the possibility of correcting on the go, but also of making more obvious mistakes.

• Semantic ambiguity: Many words sound the same but have different meanings: “bank” in its multiple senses, “vote” and “throw away”… Context, which is often only resolved several seconds later, is unreachable for models that work in real-time.

• Detection of proper names: Identifying the names of people, products, companies, or places correctly is particularly complex. Many proper names do not exist in training corpora, can sound similar to common words, or be pronounced with very diverse accents, increasing the error rate and potentially generating critical misunderstandings.

• Resource consumption: To function live, models must be efficient and respond with milliseconds to spare, without sacrificing too much accuracy. The quest for balance between latency and precision is a constant in the development of these solutions.

The extra challenge of real-time

Perhaps the most decisive difference between transcribing recorded audio and live transcription is the impossibility of looking “into the future.” In a recording, AI can process, analyze, rewind, and correct as many times as needed. In a live call, the transcription must move with the flow of the conversation, anticipating and correcting as necessary, but without the safety net that context provides afterward. This introduces phenomena like provisional transcriptions, where AI offers an initial interpretation and, seconds later, corrects it as it receives more information. For the user, this might seem like a stutter; for developers, it is a demonstration of the limitations that still exist in “on-the-fly” language understanding.

The multilingual challenge and “code-switching”

But there is another layer of complexity: multilingualism. In a global world, calls can start in one language and suddenly switch to another. AI must detect the change almost instantaneously, adapt to the new code, and continue transcribing without losing the thread. This phenomenon, known as “code-switching,” is especially common in international contexts or among bilingual communities. If it requires effort for a human, for a machine it represents a monumental challenge: it must not only recognize which language is being spoken but also adjust all its acoustic, phonetic, and language models in real time to avoid gross errors or inappropriate literal translations. Moreover, each language adds its own set of accents, jargon, and regional variations. Multilingual models, therefore, tend to be much larger and more complex, further complicating their deployment in low-latency scenarios.

How technology addresses the challenge… and its limits

Advances in recent years have been spectacular. From the rule-based and pattern systems of the 80s and 90s to today’s deep learning models, the improvement in error rates has been constant. Models like Whisper from OpenAI or the Nova family from Deepgram now integrate deep neural networks, attention, and transformers capable of learning directly from vast volumes of audio worldwide. But even with these technologies, the reality is that perfect automatic transcription remains elusive. Noise, abrupt changes in context, code-switching, and the need to make decisions “in real-time” force AI to take shortcuts and sometimes makes mistakes. Often, the most advanced models require a computational resource amount that makes them unfeasible for mass deployment on low-cost devices or on limited infrastructures, as is often the case in traditional telephone exchanges.

Practical implications: what is at stake

A faulty transcription can have notable consequences: from frustrated users needing to repeat their information to serious errors in critical services, such as emergencies, banking, or health. Therefore, efforts to improve the accuracy of transcription models are not just a technological race; they are also a commitment to user experience, accessibility, and system reliability. No less relevant is the ethical and legal challenge: recorded and transcribed conversations must protect the privacy of the interlocutors and comply with regulations like GDPR. Additionally, there is a risk that biases or systematic errors in the models end up perpetuating inequalities, for example, by not understanding well those with a marked accent or those using less represented variants of the language.

Conclusion: the long road to perfect understanding

The dream of an AI that “listens and understands” like a human remains, for the most part, just that: a dream. But it is also a reminder of the richness and complexity of our own language. Each advancement in automatic transcription brings us a little closer to that ideal, but it also shows us the limits of what technology can – and cannot – capture of human communication. The next time a virtual assistant misspells your name or asks you to repeat, remember that behind that failure, there is a technical and human struggle to bring machines closer to the chaotic, rich, and unpredictable reality of our conversations. Because, although AI progresses rapidly, fully understanding each other remains one of the greatest challenges of our digital era.