Embedded Large-Scale Handwritten Chinese Character Recognition

As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices. To achieve acceptable accuracy, we paid particular attention to data collection conditions, representativeness of writing styles, and training regimen. We found that, with proper care, even larger inventories are within reach. Our experiments show that accuracy only degrades slowly as the inventory increases, as long as we use training data of sufficient quality and in sufficient quantity.Comment: 5 pages, 7 figure

A Novel Approach to Unsupervised Grapheme-to-Phoneme Conversion

Automatic, data-driven grapheme-to-phoneme conversion is a challenging but often necessary task. The top-down strategy implicitly adopted by traditional inductive learning techniques tends to dismiss relevant contexts when they have been seen too infrequently in the training data. This paper proposes instead a bottom-up approach which, by design, exhibits better generalization properties. For each out-of-vocabulary word, a neighborhood of locally relevant pronunciations is constructed through latent semantic analysis of the appropriate graphemic form. Phoneme transcription then proceeds via locally optimal sequence alignment and maximum likelihood position scoring. This method was successfully applied to the speech synthesis of proper names with a large diversity of origin

Statistical language model adaptation: review and perspectives

Speech recognition performance is severely affected when the lexical, syntactic, or semantic characteristics of the discourse in the training and recognition tasks differ. The aim of language model adaptation is to exploit specific, albeit limited, knowledge about the recognition task to compensate for this mismatch. More generally, an adaptive language model seeks to maintain an adequate representation of the current task domain under changing conditions involving potential variations in vocabulary, syntax, content, and style. This paper presents an overview of the major approaches proposed to address this issue, and offers some perspectives regarding their comparative merits and associated tradeoffs. Ó 2003 Elsevier B.V. All rights reserved. 1