Jargonic Sets New Standards for Japanese ASR
Explore BenchmarksJargonic Sets New Standards for Japanese ASR
Explore Benchmarks
aiOla’s research team is a world-class powerhouse in voice and speech AI, with seven PhDs from top companies and academic institutions.
Led by Gil Hetz PhD, Professor Yossi Keshet and Professor Bhiksha Raj, our experts are redefining industry standards, pioneering breakthroughs in ASR and Conversational AI. Their cutting-edge work drives aiOla’s unmatched accuracy and adaptability, empowering enterprises to unlock the full potential of spoken data.
After setting new benchmarks in English, Spanish, French, and more, Jargonic V2 now leads in Japanese as well—delivering not just superior transcription accuracy, but also unmatched recall of specialized terms across industries like manufacturing, logistics, healthcare, and finance.
An enterprise-grade speech recognition model that outperforms all competitors across both academic benchmarks and real-world business environments. In comprehensive testing, Jargonic achieved the highest accuracy on standard datasets and superior jargon recognition capabilities, establishing it as the industry’s most accurate speech-to-text solution available.
A novel multi-head efficient decoding approach for transformer-based Automatic Speech Recognition (ASR), improving inference speed and accuracy.
A privacy-focused speech recognition approach that enables entity recognition while anonymizing sensitive information, meeting enterprise-grade security and compliance requirements.
An advanced framework that integrates named entity recognition (NER) into speech-to-text pipelines, enhancing real-time voice data processing.
A novel method for combining multiple language models to improve speech recognition across specialized industries, ensuring more accurate jargon recognition.
An advanced adaptation model that enhances ASR performance in specialized domains by guiding recognition with contextual keyword injection.
A cutting-edge technique enabling open-vocabulary keyword spotting using adaptive instance normalization to enhance real-time voice interaction and command execution.
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