By Seamus McAteer – Feb 7, 2025
The AI technology landscape is evolving at breakneck speed. While DeepSeek dominates the news cycle, this rapid innovation is particularly evident in speech-to-speech translation. Automatic Speech Recognition (ASR), Text-to-Speech (TTS), and translation technologies have made massive leaps in the past 18 months, driven by new generative models. Simultaneously, the release of multi-modal LLMs promises to make real-time speech-to-speech translation more accurate, faster, and less prone to errors and latency.
As models are commoditized, value will shift to applications. To survive and thrive in this environment, speech-to-speech translation companies must focus on building best-in-class products that go well beyond the underlying models. This means honing in on four critical areas: verticalization, workflow alignment, integrations, and user experience (UX). Let’s break down why these elements are essential and how they apply to the speech-to-speech translation space.
Hyper-scalers—LLMs and enterprise cloud platforms—along with large platform players, will dominate the market for horizontal speech-to-speech solutions. However, these solutions often fail to meet the specific needs of users across various verticals. We’ve seen this before. For example, YouTube provides captions and subtitles for free that meet the needs of many, but that hasn’t stymied a large market for media accessibility and localization met by companies like 3Play Media and Rev.
The market for automated dubbing will be more nuanced. What meets an acceptable threshold for Hollywood studios, enterprise users, edtech platforms, or individual creators is very different. While we have seen a lot of trials of AI dubbing by Hollywood studios, the fact that it is not quite as natural-sounding as a voice actor—and concerns about raising the hackles of the Screen Actors Guild—has meant there has been no large-scale adoption. YouTube and Meta will lead dubbing for creators. But dubbing for the vast majority of professionally produced content will be addressed by solutions that accommodate bulk processing, scalable human review, and editing.
AI models don’t exist in a vacuum—they must integrate seamlessly into user workflows. Understanding the context in which your technology will be used is crucial for building a product that resonates with your audience.
In the dubbing process, content goes through multiple stages: transcription, proofing, translation, editing, dubbing, and review. Each step involves different stakeholders, from translators to audio engineers and producers. Your AI solution must align with this workflow, perhaps by integrating with existing tools for transcription and captioning or providing an interface that allows for easy collaboration between teams and definitions of user rights and roles.
For live meeting translation, the workflow might look like this: capturing the audio stream, processing it through your cloud platform, and delivering real-time translations via a shareable link or integration with an online meeting or telehealth platform. Complexities arise depending on the use case. For example, in certain applications, such as an interaction between a healthcare professional and a senior, consecutive interpretation may be preferred over simultaneous translation. In contrast, at a conference, low-latency simultaneous interpretation delivered back to a mobile app will be the best approach.
No product is an island, especially in the enterprise world. To be useful, an AI solution must integrate seamlessly with the tools and platforms your customers already use. The nature of these integrations will vary depending on the vertical and use case.
Enterprises may use one of a large number of media asset management platforms on the market. A video localization platform will generate numerous files. For example, the Speechlab dubbing process generates video with and without background audio, corresponding audio files, SRT captions, and subtitles for the source and target languages. These files need to be linked to the source file in the asset management system. A language management system may include lexicons and rules that need to be applied.
If your target customer is a corporation needing live translation for meetings, you’ll need to integrate with popular online meeting platforms like Zoom, Microsoft Teams, or Google Meet. For healthcare, integration with telehealth platforms like Doximity or Teladoc will be required, along with assurances that any infrastructure is compatible with HIPAA regulations regarding the processing and storage of patient data. For live events, integrating with an AV platform via an RTP endpoint may be essential.
Integrations reduce friction and make it easier for customers to adopt your solution. They also demonstrate that you understand the ecosystem in which your customers operate, which can be a key differentiator.
The user experience (UX) of your AI product can make or break its success. It’s not just about a sleek interface—it’s about designing for the specific needs of your end users, who may have vastly different levels of technical expertise and workflows.
An audio engineering professional will need advanced controls for fine-tuning voice parameters, while an individual creator might prioritize simplicity and ease of use. Similarly, a translation professional might require tools for editing and proofing, whereas a video editor might focus on seamless integration with their editing software.
The UX for a conference interpretation platform will differ significantly from one designed for a healthcare setting. In a conference, users might need real-time translations delivered via an app or earpiece, while in healthcare, the interface must be intuitive enough for seniors or non-tech-savvy users to navigate easily.
While users may jump through hoops to access a capability that is essential and otherwise unavailable, the promise of AI is effortless usability. In a market where core technology and barriers to building applications fall, UX will define the winners. This is more than design—it may also include speed, latency, and the ability to tune models for the use case.
Despite rapid advancements in LLMs and multi-modal AI, we are still in the early days of speech-to-speech translation. The idea of a single, all-encompassing model capable of handling every use case—whether dubbing, live interpretation, or customer service—remains a pipe dream. Similarly, the notion of AI agents autonomously managing every aspect of these workflows is far from reality.
Instead, the future will be defined by specialized solutions that combine cutting-edge AI with deep vertical expertise, seamless integrations, and user-centric design. Companies that embrace this approach will not only survive the commoditization of core technology but also thrive by delivering real value to their customers.
