End-to-end (E2E) Automatic Speech Recognition (ASR) models are trained using paired audio-text samples that are expensive to obtain, since high-quality ground-truth data requires human annotators. Voice search applications, such as digital media players, leverage ASR to allow users to search by voice as opposed to an on-screen keyboard. However, recent or infrequent movie titles may not be sufficiently represented in the E2E ASR system’s training data, and hence, may suffer poor recognition.
In this paper, we propose a phonetic correction system that consists of (a) a phonetic search based on…
Source: Read MoreÂ
