Abstract: Methods and systems for performing audio synchronization with corresponding textual transcription and determining confidence values of the timing-synchronization are provided. Audio and a corresponding text (e.g., transcript) may be synchronized in a forward and reverse direction using speech recognition to output a time-annotated audio-lyrics synchronized data. Metrics can be computed to quantify and/or qualify a confidence of the synchronization. Based on the metrics, example embodiments describe methods for enhancing an automated synchronization process to possibly adapted Hidden Markov Models (HMMs) to the synchronized audio for use during the speech recognition. Other examples describe methods for selecting an appropriate HMM for use.

Abstract: The present application discloses systems and methods for verifying ownership of a printed publication. One embodiment includes receiving a first image of a portion of a printed publication from a first communications device associated with a user, the first image including an identifier uniquely associated with the user. The first image may be analyzed to verify the printed publication and to confirm that identifier is associated with the user. Some embodiments also include authenticating digital content associated with the identified publication for viewing on and/or downloading to one or more communications devices associated with the user whose associated identifier appears in the first image.

Abstract: Methods and systems for performing audio synchronization with corresponding textual transcription and determining confidence values of the timing-synchronization are provided. Audio and a corresponding text (e.g., transcript) may be synchronized in a forward and reverse direction using speech recognition to output a time-annotated audio-lyrics synchronized data. Metrics can be computed to quantify and/or qualify a confidence of the synchronization. Based on the metrics, example embodiments describe methods for enhancing an automated synchronization process to possibly adapted Hidden Markov Models (HMMs) to the synchronized audio for use during the speech recognition. Other examples describe methods for selecting an appropriate HMM for use.

Abstract: An automated speech recognition filter is disclosed. The automated speech recognition filter device provides a speech signal to an automated speech platform that approximates an original speech signal as spoken into a transceiver by a user. In providing the speech signal, the automated speech recognition filter determines various models representative of a cumulative signal degradation of the original speech signal from various devices along a transmission signal path and a reception signal path between the transceiver and a device housing the filter. The automated speech platform can thereby provide an audio signal corresponding to a context of the original speech signal.

Abstract: An automated speech recognition filter is disclosed. The automated speech recognition filter device provides a speech signal to an automated speech platform that approximates an original speech signal as spoken into a transceiver by a user. In providing the speech signal, the automated speech recognition filter determines various models representative of a cumulative signal degradation of the original speech signal from various devices along a transmission signal path and a reception signal path between the transceiver and a device housing the filter. The automated speech platform can thereby provide an audio signal corresponding to a context of the original speech signal.

Abstract: An automated speech recognition filter is disclosed. The automated speech recognition filter device provides a speech signal to an automated speech platform that approximates an original speech signal as spoken into a transceiver by a user. In providing the speech signal, the automated speech recognition filter determines various models representative of a cumulative signal degradation of the original speech signal from various devices along a transmission signal path and a reception signal path between the transceiver and a device housing the filter. The automated speech platform can thereby provide an audio signal corresponding to a context of the original speech signal.

Abstract: An automated speech recognition filter is disclosed. The automated speech recognition filter device provides a speech signal to an automated speech platform that approximates an original speech signal as spoken into a transceiver by a user. In providing the speech signal, the automated speech recognition filter determines various models representative of a cumulative signal degradation of the original speech signal from various devices along a transmission signal path and a reception signal path between the transceiver and a device housing the filter. The automated speech platform can thereby provide an audio signal corresponding to a context of the original speech signal.