Amazon Technologies, Inc.

Faisal Ladhak

Markus Dreyer

Ariya Rastrow

Lambert Leo Mathias

Ankur Gandhe

Björn Hoffmeister

Anjishnu Kumar

Arpit Gupta

William Clinton Dabney

A voice user interface (VUI) system use collaborative filtering to expand its own knowledge base. The system is designed to improve the accuracy and performance of the Natural Language Understanding (NLU) processing that underlies VUIs. The system leverages the knowledge of system users to crowdsource new information.

Voice user interface knowledge acquisition system

Neural networks may be used in certain automatic speech recognition systems. To improve performance at these neural networks, the present system converts the lattice into a matrix form, thus maintaining certain information included in the lattice that might otherwise be lost while also placing the lattice in a form that may be manipulated by other components to perform operations such as checking ASR results. The matrix representation of the lattice may be transformed into a vector representation by calculations performed at a recurrent neural network (RNN). By representing the lattice as a vector representation the system may perform additional operations, such as ASR results confirmation.

Lattice decoding and result confirmation using recurrent neural networks

An automatic speech recognition (ASR) system may convert an ASR output lattice into a matrix form, thus maintaining certain information included in the lattice that might otherwise be lost in an N-best list output. The matrix representation of the lattice may be encoded using a recurrent neural network (RNN) to create a vector representation of the lattice. The vector representation may then be used by the system to perform additional operations, such as ASR results confirmation.

Lattice encoding using recurrent neural networks

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Seattle, WA, United States of America

Faisal Ladhak