“Real-Time Continuous Transcription With Live Transcribe”, 2019-02-04 (; similar):
The World Health Organization (WHO) estimates that there are 466 million people globally that are deaf and hard of hearing. A crucial technology in empowering communication and inclusive access to the world’s information to this population is automatic speech recognition (ASR), which enables computers to detect audible languages and transcribe them into text for reading. Google’s ASR is behind automated captions in Youtube, presentations in Slides and also phone calls…Today, we’re announcing Live Transcribe, a free Android service that makes real-world conversations more accessible by bringing the power of automatic captioning into everyday, conversational use. Powered by Google Cloud, Live Transcribe captions conversations in real-time, supporting over 70 languages and more than 80% of the world’s population. You can launch it with a single tap from within any app, directly from the accessibility icon on the system tray.
…Relying on cloud ASR provides us greater accuracy, but we wanted to reduce the network data consumption that Live Transcribe requires. To do this, we implemented an on-device neural network-based speech detector, built on our previous work with AudioSet. This network is an image-like model, similar to our published VGGish model, which detects speech and automatically manages network connections to the cloud ASR engine, minimizing data usage over long periods of use.
…Known as the cocktail party problem, understanding a speaker in a noisy room is a major challenge for computers. To address this, we built an indicator that visualizes the volume of user speech relative to background noise. This also gives users instant feedback on how well the microphone is receiving the incoming speech from the speaker, allowing them to adjust the placement of the phone…Potential future improvements in mobile-based automatic speech transcription include on-device recognition, speaker-separation, and speech enhancement. Relying solely on transcription can have pitfalls that can lead to miscommunication. Our research with Gallaudet University shows that combining it with other auditory signals like speech detection and a loudness indicator, makes a tangibly meaningful change in communication options for our users.