This paper summarizes recent work at Microsoft on the development of novel direct models. The key characteristic of our approaches is the use of long-span segment level features that relate acoustic properties directly to words. In this approach, the frame-level Markov assumption is replaced by the segment level Markov property, allowing us to extract long-span features. A key issue we address is the de\ufb01nition of generalizable features which allow us to model unseen words. We review two recently developedmodels that have this property: Flat Direct Models (FDMs), and Segmental CRFs (SCRFs). The \ufb01rst operates in a log-linear framework, and uses utterance level features. The second is also a log-linear model, but de\ufb01nes features at the word-segment level. We present new experimental results comparing the two approaches. We \ufb01nd that both show consistent improvements over a baseline system, and that the extra context available to the FDM enables slightly better performance in a rescoring context. This gain comes at the expense of applicability to \ufb01rst pass decoding, for which the SCRF is better suited.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper summarizes recent work at Microsoft on the development of novel direct models. The key characteristic of our approaches is the use of long-span segment level features that relate acoustic properties directly to words. In this approach, the frame-level Markov assumption is replaced by the segment level Markov property, allowing us to extract long-span 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