Continual Learning in Automatic Speech Recognition

By Samik Sadhu, Hynek Hermansky. 2020

39 citations recorded by the source corpus.

We emulate continual learning observed in real life, where new training data, which represent new application domain, are used for gradual improvement of an Automatic Speech Recog-nizer(ASR) trained on old domains. The data on which the original classifier was trained is no longer required and we observe no loss of performance on the original domain. Further, on previously unseen domain, our technique appears to yield slight advantage over offline multi-condition training. The proposed learning technique is consistent with our previously studied ad hoc stream attention based multi-stream ASR

Subfields: Artificial Intelligence, Signal Processing

Citations in the corpus

• Online Coreset Selection for Rehearsal-based Continual Learning (2021, neutral): “In light of this need, continual learning (CL) [40], or lifelong learning, tackles a learning scenario where a model continuously learns over a sequence of tasks [18, 25] within a broad research area, such as classification [15, 9], image generation [47], language learning [24, 4], clinical applicat”