Improving ASR accuracy in educational settings by addressing biases related to accents, background noise and recording devices.

Ensuring privacy and data security for mission-critical clients by operating ASR models locally or within secure ecosystems.

Providing a fair and unbiased assessment of the accuracy and inclusivity of available speech-to-text services. Making available a tool that allows potential customers or users of ASR to understand which are the best services out there.

Through accessibility and robustness, opening up new opportunities for ASR in public sector settings such as hospitals and clinics, which are otherwise hard to address due to imperfect noise environments and biased existing ASR tools.

By focusing on more advanced accuracy metrics which take into account not just differences in words, but also differences in context and meaning.

Improving customer experiences by integrating ASR into customer contact centre operations, for sentiment analysis, voice-interactive chatbots, transcription for training, pronunciation assessments and more.

Using ASR features to enable voice search, voice commands, voice biometrics, digital assistants, integrated AI and other stand-out consumer offerings.