Microsoft Technology Licensing, LLC

Alex Acero

Li Deng

Yun-Cheng Ju

Dong Yu

Ivan Jelev Tashev

Ye-Yi Wang

Xiaodong He

Michael Lewis Seltzer

Xiao Li

Xiaolong Li

John Sie Yuen Lee

A method of providing automatic reading tutoring is disclosed. The method includes retrieving a textual indication of a story from a data store and creating a language model including constructing a target context free grammar indicative of a first portion of the story. A first acoustic input is received and a speech recognition engine is employed to recognize the first acoustic input. An output of the speech recognition engine is compared to the language model and a signal indicative of whether the output of the speech recognition matches at least a portion of the target context free grammar is provided.

Automatic reading tutoring using dynamically built language model

The presentation of location information to a user that is distracted by traveling can result in the user quickly forgetting, or never even comprehending, key parts of the location information, such as the street number. Identification can be made of intersections and points of interest near the user's destination, which can then be provided instead of, or in addition to, the address, thereby increasing user comprehension and retention, especially when distracted. Map data can be parsed into addresses, intersections and points of interest databases. These databases can be accessed to identify proximate intersections and points of interest, which can then be filtered and subsequently ranked to identify one intersection, one point of interest, or both, that can be presented to the user to aid the user in comprehending and retaining the location information even when distracted.

Conveying locations in spoken dialog systems

A training system for a speech recognition application is disclosed. In embodiments described, the training system is used to train a classification model or language model. The classification model is trained using an adaptive language model generated by an iterative training process. In embodiments described, the training data is recognized by the speech recognition component and the recognized text is used to create the adaptive language model which is used for speech recognition in a following training iteration.

Training system for a speech recognition application

A method and apparatus for training an acoustic model are disclosed. A training corpus is accessed and converted into an initial acoustic model. Scores are calculated for a correct class and competitive classes, respectively, for each token given the acoustic model. From this score a misclassification measure is calculated and then a loss function is calculated from the misclassification measure. The loss function also includes a margin value that varies over each iteration in the training. Based on the calculated loss function the acoustic model is updated, where the loss function with the margin value is minimized. This process repeats until such time as an empirical convergence is met.

Incrementally regulated discriminative margins in MCE training for speech recognition

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Bellevue, WA, United States of America

Alex Acero