Amazon Technologies, Inc.

Björn Hoffmeister

Ariya Rastrow

Ankur Gandhe

Lambert Leo Mathias

Markus Dreyer

Faisal Ladhak

Shiv Naga Prasad Vitaladevuni

Ming Sun

Denis Sergeyevich Filimonov

Sree Hari Krishnan Parthasarathi

Varun Kumar Nagaraja

Roland Maas

Brian King

New facts are added to a query answering system that uses automatic speech recognition (ASR) processing. Incoming ASR requests may be compared against each other to check accuracy of semantic processing. Further, accuracy of ASR transcription may be confirmed using a confidence check. Text obtained from internet or other sources may be processed with trained classifiers (which may be specific to a given relation) to identify text corresponding to the relation and to identify the entities referred to in the relation. The text, entities, and relation may then be saved and used to respond to future queries.

Confidence checking for speech processing and query answering

An automatic speech recognition (ASR) system uses recurrent neural network (RNN) encoding to create a feature vector corresponding to a word sequence ASR result where the feature vector incorporates data from different hierarchies (i.e., frame level, phone level, etc.) of the ASR processing. The feature vector may be used with a trained classifier to confirm that the ASR result was correct, or to otherwise assign a confidence score to the ASR results.

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

A language model for automatic speech processing, such as a finite state transducer (FST) may be configured to incorporate information about how a particular word sequence (N-gram) may be used in a similar manner from another N-gram. A score of a component of the FST (such as an arc or state) relating to the first N-gram may be based on information of the second N-gram. Further, the FST may be configured to have an arc between a state of the first N-gram and a state of the second N-gram to allow for cross N-gram back off, rather than backoff from a larger N-gram to a smaller N-gram during traversal of the FST during speech processing.

Automatic speech recognition incorporating word usage information

A revised support vector machine (SVM) classifier is offered to distinguish between true keywords and false positives based on output from a keyword spotting component of a speech recognition system. The SVM operates on a reduced set of feature dimensions, where the feature dimensions are selected based on their ability to distinguish between true keywords and false positives. Further, support vectors pairs are merged to create a reduced set of re-weighted support vectors. These techniques result in an SVM that may be operated using reduced computing resources, thus improving system performance.

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Björn Hoffmeister