Users often experience poor audio quality due to background noise in virtual meeting platforms, affecting communication clarity.
Background Noise Cancellation (BNC)
Active Noise Cancellation (ANC), Digital Signal Processing (DSP), Signal-to-Noise Ratio (SNR), Multi-Vector Noise Suppression (MVNS)
Significantly improves audio quality by reducing unwanted background noise, providing a clearer and more professional meeting environment.
Software applications require more intuitive voice command features to enhance user interaction and accessibility.
Speech Recognition (SR)
Automatic Speech Recognition (ASR), Voice User Interface (VUI), Speech-to-Text (STT), Hidden Markov Model(HMM), Deep Neural Network (DNN)
Enables seamless voice interactions, allowing for more natural user interfaces and improving overall user experience.
Protecting user data and ensuring privacy in voice-enabled applications is increasingly critical.
Speaker Voice Recognition (SVR)
Speaker Verification (SV), Speaker Identification(SI), Text-to-Speech (TTS), Mel-Frequency Cepstral Coefficients(MFCC)
Enhances security protocols by authenticating users through voice, ensuring a secure and personalized experience.
Software applications, particularly communication tools, often suffer from degraded quality due to packet loss in poor network conditions.
Generative AI for Packet Loss Correction
Enhances audio and video quality by reconstructing lost packets, ensuring clear and uninterrupted communication even in unstable network environments.
As software platforms expand globally, there is a need to support multiple languages and dialects effectively.
Multilingual Speech Recognition
Automatic Speech Recognition (ASR), Language Processing Algorithms
Supports a wide range of languages, enabling software companies to provide consistent and effective communication tools worldwide.