Abstract: A system and method is provided for combining active and unsupervised learning for automatic speech recognition. This process enables a reduction in the amount of human supervision required for training acoustic and language models and an increase in the performance given the transcribed and un-transcribed data.

Abstract: Disclosed herein are methods for presenting speech from a selected text that is on a computing device. This method includes presenting text on a touch-sensitive display and having that text size within a threshold level so that the computing device can accurately determine the intent of the user when the user touches the touch screen. Once the user touch has been received, the computing device identifies and interprets the portion of text that is to be selected, and subsequently presents the text audibly to the user.

Abstract: A request from a party is received by a receiver from a remote system. The request from the party is received when the party attempts to obtain a service using the remote system. A selective determination is made to request, over a network, authentication of the party by a remote biometric system. A request is sent to the remote system for the party to provide a biometric sample responsive to determining to request authentication of the party. The service is provided contingent upon authentication of the party by the remote biometric system.

Abstract: Systems and methods for processing audio are provided. The system may include a processor to convert an audio input received via a call to text. The processor may perform a comparison between a portion of the text to one or more phrases included in a table. The processor may also make a selection of at least one of a first object or a first action based on the comparison. The processor may further route the call based on the at least one of the first object or the first action.

Abstract: Example methods, apparatus and articles to manage routing in networks are disclosed. A disclosed example method includes identifying a first network element associated with a problematic network element; identifying a first maximum transmission unit of the first network element and a second maximum transmission unit of the problematic network element; determining whether the first and second maximum transmission units are different; and when the first and second maximum transmission units are different, identifying a value of a greater one of the first and second maximum transmission units and configuring the first network element and the problematic network element to each have the identified value.

Abstract: Disclosed herein are systems, computer-implemented methods, and computer-readable media for recognizing speech. The method includes receiving speech from a user, perceiving at least one speech dialect in the received speech, selecting at least one grammar from a plurality of optimized dialect grammars based on at least one score associated with the perceived speech dialect and the perceived at least one speech dialect, and recognizing the received speech with the selected at least one grammar. Selecting at least one grammar can be further based on a user profile. Multiple grammars can be blended. Predefined parameters can include pronunciation differences, vocabulary, and sentence structure. Optimized dialect grammars can be domain specific. The method can further include recognizing initial received speech with a generic grammar until an optimized dialect grammar is selected. Selecting at least one grammar from a plurality of optimized dialect grammars can be based on a certainty threshold.

Abstract: Disclosed herein are systems, methods, and computer-readable storage media for selecting a speech recognition model in a standardized speech recognition infrastructure. The system receives speech from a user, and if a user-specific supervised speech model associated with the user is available, retrieves the supervised speech model. If the user-specific supervised speech model is unavailable and if an unsupervised speech model is available, the system retrieves the unsupervised speech model. If the user-specific supervised speech model and the unsupervised speech model are unavailable, the system retrieves a generic speech model associated with the user. Next the system recognizes the received speech from the user with the retrieved model. In one embodiment, the system trains a speech recognition model in a standardized speech recognition infrastructure. In another embodiment, the system handshakes with a remote application in a standardized speech recognition infrastructure.

Abstract: State-of-the-art speech recognition systems are trained using transcribed utterances, preparation of which is labor-intensive and time-consuming. The present invention is an iterative method for reducing the transcription effort for training in automatic speech recognition (ASR). Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples and then selecting the most informative ones with respect to a given cost function for a human to label. The method comprises automatically estimating a confidence score for each word of the utterance and exploiting the lattice output of a speech recognizer, which was trained on a small set of transcribed data. An utterance confidence score is computed based on these word confidence scores; then the utterances are selectively sampled to be transcribed using the utterance confidence scores.

Abstract: A bookmark system is provided that accommodates a plurality of bookmarks that are able to be selectively added and removed from a centralized head. Each bookmark is capable of sliding independently along a pin and is designed to fit between two pages of a book. The bookmark includes a member for secure integration to the book to ensure that it and its associated bookmarks remain in place during use of the book.

Abstract: An interactive voice and data response system that directs input to a voice, text, and web-capable software-based router, which is able to intelligently respond to the input by drawing on a combination of human agents, advanced speech recognition and expert systems, connected to the router via a TCP/IP network. The digitized input is broken down into components so that the customer interaction is managed as a series of small tasks performed by a pool of human agents, rather than one ongoing conversation between the customer and a single agent. The router manages the interactions and keeps pace with a real-time conversation. The system utilizes both speech recognition and human intelligence for purposes of interpreting customer utterances or customer text, where the role of the human agent(s) is to input the intent of caller utterances, and where the computer system—not the human agent—determines which response to provide given the customer's stated intent (as interpreted/captured by the human agents).

Abstract: A bookmark system is provided that accommodates a plurality of bookmarks that are able to be selectively added and removed from a centralized head. Each bookmark is capable of sliding independently along a pin and is designed to fit between two pages of a book. The bookmark includes a member for secure integration to the book to ensure that it and its associated bookmarks remain in place during use of the book.

Abstract: An interactive voice and data response system that directs input to a voice, text, and web-capable software-based router, which is able to intelligently respond to the input by drawing on a combination of human agents, advanced speech recognition and expert systems, connected to the router via a TCP/IP network. The digitized input is broken down into components so that the customer interaction is managed as a series of small tasks rather than one ongoing conversation. The router manages the interactions and keeps pace with a real-time conversation. The system utilizes both speech recognition and human intelligence for purposes of interpreting customer utterance or customer text. The system may use more than one human agent, or both human agents and speech recognition software, to interpret simultaneously the same component for error-checking and interpretation accuracy.