Abstract: Disclosed herein are systems, methods, and computer-readable storage devices for fetching speech processing models based on context changes in advance of speech requests using the speech processing models. An example local device configured to practice the method, having a local speech processor, and having access to remote speech models, detects a change in context. The change in context can be based on geographical location, language translation, speech in a different language, user language settings, installing or removing an app, and so forth. The local device can determine a speech processing model that is likely to be needed based on the change in context, and that is not stored on the local device. Independently of an explicit request to process speech, the local device can retrieve, from a remote server, the speech processing model for use on the mobile device.

Abstract: Disclosed herein are systems, methods, and computer-readable storage devices for fetching speech processing models based on context changes in advance of speech requests using the speech processing models. An example local device configured to practice the method, having a local speech processor, and having access to remote speech models, detects a change in context. The change in context can be based on geographical location, language translation, speech in a different language, user language settings, installing or removing an app, and so forth. The local device can determine a speech processing model that is likely to be needed based on the change in context, and that is not stored on the local device. Independently of an explicit request to process speech, the local device can retrieve, from a remote server, the speech processing model for use on the mobile device.

Abstract: Disclosed herein are systems, methods, and computer-readable storage devices for fetching speech processing models based on context changes in advance of speech requests using the speech processing models. An example local device configured to practice the method, having a local speech processor, and having access to remote speech models, detects a change in context. The change in context can be based on geographical location, language translation, speech in a different language, user language settings, installing or removing an app, and so forth. The local device can determine a speech processing model that is likely to be needed based on the change in context, and that is not stored on the local device. Independently of an explicit request to process speech, the local device can retrieve, from a remote server, the speech processing model for use on the mobile device.

Abstract: Disclosed herein are systems, methods, and computer-readable storage devices for building classifiers in a semi-supervised or unsupervised way. An example system implementing the method can receive a human-generated map which identifies categories of transcriptions. Then the system can receive a set of machine transcriptions. The system can process each machine transcription in the set of machine transcriptions via a set of natural language understanding classifiers, to yield a machine map, the machine map including a set of classifications and a classification score for each machine transcription in the set of machine transcriptions. Then the system can generate silver annotated data by combining the human-generated map and the machine map. The algorithm can include different branches for when the machine transcription is available, when partial results are available, when no results are found for the machine transcription, and so forth.

Abstract: Disclosed herein are systems, methods, and computer-readable storage devices for fetching speech processing models based on context changes in advance of speech requests using the speech processing models. An example local device configured to practice the method, having a local speech processor, and having access to remote speech models, detects a change in context. The change in context can be based on geographical location, language translation, speech in a different language, user language settings, installing or removing an app, and so forth. The local device can determine a speech processing model that is likely to be needed based on the change in context, and that is not stored on the local device. Independently of an explicit request to process speech, the local device can retrieve, from a remote server, the speech processing model for use on the mobile device.

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for making a multi-factor decision whether to process speech or language requests via a network-based speech processor or a local speech processor. An example local device configured to practice the method, having a local speech processor, and having access to a remote speech processor, receives a request to process speech. The local device can analyze multi-vector context data associated with the request to identify one of the local speech processor and the remote speech processor as an optimal speech processor. Then the local device can process the speech, in response to the request, using the optimal speech processor. If the optimal speech processor is local, then the local device processes the speech. If the optimal speech processor is remote, the local device passes the request and any supporting data to the remote speech processor and waits for a result.

Abstract: An application program interface capable of routing commands received from external devices to the appropriate system application that is runtime configurable is disclosed. At runtime, the application program interface receives enrollment signals from external interface packages used to provide communications to the external devices and dynamically updates a first routing table. Additionally, at runtime, the application program interface receives enrollment signals from system application packages used to perform system commands and dynamically updates a second routing table. The routing tables are used to route command request and responses between the external interface and system application packages and thus, provides a communication interface between external devices and system application software. Since the routing tables are configured at runtime, the application program interface can be re-configured without re-compiling the software used to implement it.

Abstract: Methods for parsing and validating a command message produced by a network element in a network utilizes table-drive approach. The messages are parsed by a parsing engine in object code stored at the network element and which operatively breaks down each of the messages into its constituent parameters. As each parameter is parsed, it is compared to a set of allowed values in object code stored in tables at the network element. Each validated parameter is entered into a data structure so that a valid message is populated. The methods allow for quick parsing and validation of existing messages and simple updating of the tables to allow for validation of new messages.