Amazon Technologies, Inc.

Ruiqi Luo

Jingqian Zhao

Prathap Ramachandra

Liu Yang

Ponnu Jacob

Adam Kalman

Wenbo Yan

Ajay Kumar Soni

Uday Kumar Kollu

Adriano Devillaine

Xi Chelsea Chen

Xi Chen

Amitpal Singh Bhutani

Charlotte Alizerine Dzialo

Nalledath P Vinodkrishnan

Kyung Jae Lee

Krupal Maddipati

Yunqiang Zhu Zhu

Simone Filice

Mohammad Alnuaimat

Meng Xie

A speech processing system uses contextual data to determine the specific domains, subdomains, and applications appropriate for taking action in response to spoken commands and other utterances. The system can use signals and other contextual data associated with an utterance, such as location signals, content catalog data, data regarding historical usage patterns, data regarding content visually presented on a display screen of a computing device when an utterance was made, other data, or some combination thereof.

Early invocation for contextual data processing

Techniques are described and relate to providing computing services using embeddings of a transformer-based encoder. In an example, a computer system generates, by using a machine learning (ML) transformer, an embedding vector based at least in part on text. The computer system stores the embedding vector and an association between the embedding vector and the text in a data store. Further, the computer system determines that a task is to be performed based at least in part on natural language understanding (NLU) of the text. The computer system receives the embedding vector from the data store based at least in part on the association between the embedding vector and the text. The task is performed based at least in part on the embedding vector after being received from the data store.

Computing services using embeddings of a transformer-based encoder

Dynamic context-based routing of speech processing

A new model is introduced into a particular domain that receives a routing of a dialog from a speech processing component. A method associated with the model includes running a set of test utterances through the speech processing component that enables a spoken language dialog with a user to establish a base line score associated with processing for the set of test utterances. The speech processing component determines an intent of the user and routes the spoken language dialog to a network-based domain based on the intent. The method includes establishing an automatic test run of the set of test utterances to obtain a current score and, when a threshold associated with a difference between the current score and the base line score is breached, switching, at the network-based domain, from the false accept detection model to a second model.

Spoken language quality automatic regression detector background

A new model is introduced into a particular domain that receives a routing of a dialog from a speech processing component. The speech processing component is engaged in the dialog with a user and the speech processing component routes the dialog to the particular network-based domain according to a determination by the speech processing component that the user has an intent to perform a task handled by the domain. The model detects, at the domain, whether the user has the proper intent associated with the domain by using the user utterance in its entirety to yield a detection result. When the user does not have the proper intent based on the detection result, the domain drops the user utterance.

Detecting false accepts in a shopping domain for handling a spoken dialog

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Seattle, WA, United States of America

Ruiqi Luo