Abstract: A method and system can control a speech recognition system in a vehicle. The method includes monitoring adaptive feature data about interactions between a user and the speech recognition system. The method includes determining a first group of samples of the adaptive feature data and creating a control chart based on the first group of samples. The control chart includes a control limit. The method further includes determining a second group of samples of the adaptive feature data after creating the control chart. Furthermore, the method includes calculating an arithmetic mean of each sample of the second group of samples to determine a sample mean, comparing the sample mean to the control limit in order identify unexpected performance of the speech recognition system. The method includes adjusting the speech recognition system based on the identified unexpected performance if the unexpected performance is identified.

Abstract: Adaptation methods and systems are provided for a speech system of a vehicle. In one embodiment a method comprises: receiving speech data; determining a speech pace based on the speech data; determining a user model based on the speech pace; and generating adaptation parameters for at least one of a speech recognition system and a dialog manager based on the user model.

Abstract: A method for speech recognition includes generating a speech prompt, receiving a spoken utterance from a user in response to the speech prompt, wherein the spoken utterance includes a speech artifact, and compensating for the speech artifact. Compensating for the speech artifact may include, for example, utilizing a recognition grammar that includes the speech artifact as a speech component, or modifying the spoken utterance to eliminate the speech artifact.

Abstract: A method and system can control a speech recognition system in a vehicle. The method includes monitoring adaptive feature data about interactions between a user and the speech recognition system. The method includes determining a first group of samples of the adaptive feature data and creating a control chart based on the first group of samples. The control chart includes a control limit. The method further includes determining a second group of samples of the adaptive feature data after creating the control chart. Furthermore, the method includes calculating an arithmetic mean of each sample of the second group of samples to determine a sample mean, comparing the sample mean to the control limit in order identify unexpected performance of the speech recognition system. The method includes adjusting the speech recognition system based on the identified unexpected performance if the unexpected performance is identified.

Abstract: Methods and systems are provided for shaping speech dialog of a speech system. In one embodiment, a method includes: receiving data related to a first utterance from a user of the speech system; processing the data based on at least one attribute processing technique that determines at least one attribute of the first utterance; determining a shaping pattern based on the at least one attribute; and generating a speech prompt based on the shaping pattern.

Abstract: Methods and systems are provided for managing speech dialog of a speech system. In one embodiment, a method includes: receiving at least one first utterance from a user of the speech system; determining a user interaction style based on the at least one first utterance; and generating feedback to the user based on the interaction style.

Abstract: Methods and systems are provided for a speech system of a vehicle. In one embodiment, the method includes: generating an utterance signature from a speech utterance received from a user of the speech system without a specific need for a user identification interaction; developing a user signature for a user based on the utterance signature; and managing a dialog with the user based on the user signature.

Abstract: Methods and systems are provided for adapting a speech system. In one example a method includes: processing a spoken command with one or more models of one or more model types to achieve model results; evaluating a frequency of the model results; and selectively updating the one or more models of the one or more model types based on the evaluating.

Abstract: Methods and systems are provided for adapting a speech system of a vehicle. In one example a method includes: logging data from the vehicle; logging speech data from the speech system; processing the data from the vehicle and the data from the speech system to determine a pattern of context and a relation to user interaction behavior; and selectively updating a user profile of the speech system based on the pattern of context.

Abstract: Methods and systems are provided for adapting a speech system. In one example a method includes: logging speech data from the speech system; processing the speech data for a pattern of a user competence associated with at least one of task requests and interaction behavior; and selectively updating at least one of a system prompt and an interaction sequence based on the user competence.

Abstract: Methods and systems are provided for adapting a speech system. In one example a method includes: logging speech data from the speech system; detecting a user characteristic from the speech data; and selectively updating a language model based on the user characteristic.