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Our experiments reveal that it is beneficial to accuracy to exploit the more accurate backend model instead.
It provides a solution for applying MDT to an existing backend model trained for the speech feature vector of one's choice.
Besides additive noise, the MDT recognizer also handles convolutional noise by the channel compensation technique described in [23], which maximizes the likelihood of the recognized speech on the backend model.
The compact model shares its structure with the backend model containing the BGs in the sense that it uses the same phonetic decision tree (PDT) [35], but it has only M Gaussians which are shared among leaves of the PDT.
Before training the compact model, a state level segmentation is made using the Viterbi algorithm with the backend model, i.e., the segmentation specifies the alignment between the states and the frames of the training data.
Deal is similar to several third-party digital pacts, including ABC's and NBC's deals with Warner Bros. TV: The network holds onto streaming rights — which is essentially an ad-supported business, much like on-air TV — while the studio retains electronic sell-through (a backend model similar to syndication and DVD).
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They modify either the frontend signal preprocessing or the backend acoustic model of the recognizer.
As an alternative, researchers have proposed frontend MDT or have made oversimplifying independence assumptions for the backend acoustic model.
Here, the state-level segmentation of the training data is obtained by forced alignment using the backend MIDA model of Section 6.1.2.
It is remarkable that the Gaussian-based MC MDT works as fast as the non-MDT recognizer with the same backend acoustic model under noisy conditions (row 1 versus row 3 of Table 4).
The design of the front-end as well as the backend acoustic model is based on prior study [23, 33, 37] which obtained competitive accuracies on clean speech and good robustness in an MDT configuration.
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