Wednesday, March 04, 2026
Krisp has introduced Listener-side Accent Conversion, an advanced real-time voice AI technology designed to improve how people understand accented English during live conversations. The new capability works directly on a user’s device and aims to enhance communication across business meetings, customer experience (CX) operations, and voice AI agent interactions.
For many years, voice technology innovation has focused primarily on improving audio quality or documentation. For example, noise cancellation tools reduce background distractions, while transcription services accurately capture what participants say during conversations. However, even when audio clarity and transcripts are reliable, misunderstandings still occur, especially when participants speak with different accents.
To address this challenge, Krisp developed Listener-side Accent Conversion. Instead of changing how a person speaks, the technology modifies how incoming speech sounds to the listener in real time. By clarifying certain sounds that are often misheard across accents, the system helps listeners better understand the speaker while maintaining the speaker’s original voice and tone. Importantly, only the listener hears the adapted audio, ensuring that the speaker’s natural communication style remains untouched.
Accent diversity has long affected communication in several professional environments. In global meetings, participants often need to repeat themselves or slow down discussions, which can disrupt collaboration and reduce efficiency. Similarly, contact center agents who interact with customers across different accents frequently experience longer call times, increased repetition, and greater mental strain. Meanwhile, voice AI agents also face accuracy challenges when recognizing speech from a wide range of accents.
As voice communication becomes a central interface for workplace collaboration and customer engagement, comprehension is evolving into a critical system-level requirement rather than simply a personal communication challenge.
“I’ve spent more than 20 years working in tech with an Armenian accent. I know what it feels like to repeat yourself on a call, or to see someone concentrating on your pronunciation instead of your idea. Over time, that changes how freely people speak. We built Accent Conversion because communication should be about ideas, not decoding speech. If technology can remove that barrier in real time, conversations become clearer and more equal for everyone involved.” — Arto Minasyan, Co-Founder and President, Krisp
“In contact centers and AI systems, the strain isn’t abstract. Agents process multiple accents all day, often in a second language. That adds friction, time, and cognitive load to every interaction. Listener-side Accent Conversion addresses the problem at the point where speech is received, helping both humans and AI systems operate more reliably without asking anyone to change how they speak.” — Davit Baghdasaryan, Co-Founder and CEO, Krisp
Currently, Listener-side Accent Conversion is available for human-to-human meetings through Krisp’s Voice AI for Meetings application. Additionally, the same technology is being integrated into Krisp’s Call Center AI platform, enabling contact center agents to better understand customers during live calls. As a result, the system helps reduce repetition, shorten call resolution times, and improve the overall customer experience without forcing customers to adjust how they speak.
Furthermore, Krisp plans to offer the technology through its SDK, allowing developers to integrate accent conversion capabilities directly into their applications and voice AI agents. With the introduction of bidirectional Accent Conversion, Krisp now supports accent clarity on both sides of live conversations.
The system works by processing incoming audio at the phoneme level, which enables it to clarify commonly misheard sounds across accents. It operates locally on-device with latency under 200 milliseconds, making the adjustment virtually imperceptible to the human ear. Moreover, the technology requires no transcripts, performs no post-processing, and does not store raw audio, ensuring both efficiency and privacy.
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