This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the RadarUnderstand more
TrialWorth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.
Wav2Vec 2.0 is a self-supervised learning framework for speech recognition. With this framework the model is trained in two phases. First, it begins in self-supervised mode using unlabeled data and tries to achieve the best possible speech representation. Then it uses supervised fine-tuning, during which labeled data teaches the model to predict particular words or phonemes. We've used Wav2Vec and find its approach quite powerful for building automatic speech recognition models for regional languages with limited availability of labeled data.