An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Systems, methods, and computer program products for machine translation are provided. In some implementations a system is provided. The system includes a language model including a collection of n-grams from a corpus, each n-gram having a corresponding relative frequency in the corpus and an order n corresponding to a number of tokens in the n-gram, each n-gram corresponding to a backoff n-gram having an order of n-1 and a collection of backoff scores, each backoff score associated with an n-gram, the backoff score determined as a function of a backoff factor and a relative frequency of a corresponding backoff n-gram in the corpus.

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Large Language Models in Machine Translation

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

A method and apparatus are provided for building an intelligent automated assistant. Embodiments of the present invention rely on the concept of “active ontologies” (e.g., execution environments constructed in an ontology-like manner) to build and run applications for use by intelligent automated assistants. In one specific embodiment, a method for building an automated assistant includes interfacing a service-oriented architecture that includes a plurality of remote services to an active ontology, where the active ontology includes at least one active processing element that models a domain. At least one of the remote services is then registered for use in the domain.

Method and apparatus for building an intelligent automated assistant

An electronic device may capture a voice command from a user. The electronic device may store contextual information about the state of the electronic device when the voice command is received. The electronic device may transmit the voice command and the contextual information to computing equipment such as a desktop computer or a remote server. The computing equipment may perform a speech recognition operation on the voice command and may process the contextual information. The computing equipment may respond to the voice command. The computing equipment may also transmit information to the electronic device that allows the electronic device to respond to the voice command.

Electronic Devices with Voice Command and Contextual Data Processing Capabilities

The performance of traditional speech recognition systems (as applied to information extraction or translation) decreases significantly with, larger domain size, scarce training data as well as under noisy environmental conditions. This invention mitigates these problems through the introduction of a novel predictive feature extraction method which combines linguistic and statistical information for representation of information embedded in a noisy source language. The predictive features are combined with text classifiers to map the noisy text to one of the semantically or functionally similar groups. The features used by the classifier can be syntactic, semantic, and statistical.

Robust Information Extraction from Utterances

Interpretation from a first language to a second language via one or more communication devices is performed through a communication network (e.g. phone network or the internet) using a server for performing recognition and interpretation tasks, comprising the steps of: receiving an input speech utterance in a first language on a first mobile communication device; conditioning said input speech utterance; first transmitting said conditioned input speech utterance to a server; recognizing said first transmitted speech utterance to generate one or more recognition results; interpreting said recognition results to generate one or more interpretation results in an interlingua; mapping the interlingua to a second language in a first selected format; second transmitting said interpretation results in the first selected format to a second mobile communication device; and presenting said interpretation results in a second selected format on said second communication device.

Mobile speech-to-speech interpretation system

The present invention discloses a speech-to-speech translation device which allows one or more users to input a spoken utterance in one language, translates the utterance into one or more second languages, and outputs the translation in speech form. Additionally, the device allows for translation both directions, recognizing inputs in the one or more second languages and translating them back into the first language. The device recognizes and translates utterances in a limited domain as in a phrase book translation system, so the translation accuracy is essentially 100%. By limiting the domain the system increases the accuracy of the speech recognition component and thus the accuracy of the overall system. However unlike other phrase book systems, the device also allows wide variations and paraphrasing in the input, so that the user is much more likely to find the desired phrase from the stored list of phrases. The device paraphrases the input to a basic canonical form and performs the translation on that canonical form, ignoring the non-essential variations in the surface form of the input. The device can provide visual and/or auditory feedback to confirm the recognized input and makes the system usable for non-bilingual users with absolute confidence.

Speech-to-speech translation system with user-modifiable paraphrasing grammars

The present invention disclose modular speech-to-speech translation systems and methods that provide adaptable platforms to enable verbal communication between speakers of different languages within the context of specific domains. The components of the preferred embodiments of the present invention includes: (1) speech recognition; (2) machine translation; (3) N-best merging module; (4) verification; and (5) text-to-speech. Characteristics of the speech recognition module here are that the modules are structured to provide N-best selections and multi-stream processing, where multiple speech recognition engines may be active at any one time. The N-best lists from the one or more speech recognition engines may be handled either separately or collectively to improve both recognition and translation results. A merge module is responsible for integrating the N-best outputs of the translation engines along with confidence/translation scores to create a ranked list or recognition-translation pairs.

Methods for speech-to-speech translation

A virtual assistant learning system is presented. A monitoring device, a cell phone for example, observes user interactions with an environment by acquiring sensor data. The monitoring device uses the sensor data to identify the interactions, which in turn is provided to an inference engine. The inference engine leverages the interaction data and previously stored knowledge elements about the user to determine if the interaction exhibits one or more user preferences. The inference engine can use the preferences and interactions to construct queries targeting search engines to seek out possible future interactions that might be of interest to the user.

Farzad Ehsani

Papers

Robust Information Extraction from Utterances

Mobile speech-to-speech interpretation system

Speech-to-speech translation system with user-modifiable paraphrasing grammars

Methods for speech-to-speech translation

Self-learning, context aware virtual assistants, systems and methods