| AUTONOMY CORPORATION LTD. Patent applications |
| Patent application number | Title | Published |
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| 20110035356 | TRANSACTIONAL ARCHIVING OF AN ELECTRONIC DOCUMENT - Various methods and apparatus are described for archiving of an electronic document between multiple interconnected archive units of a distributed server network in geographically-dispersed locations in order to store identical copies of the electronic document at the same time. An archival portal server in the distributed server network sends a five-step, two-phase commit protocol to a selected set of two or more transaction manager instances resident on remote archive units. The archival system reconciles if an error occurs between a start of a transmission of the electronic document and a permanent archiving of that electronic document, or the electronic document is stored in a permanent data storage location within each of the archive units at an end of the two-phase commit protocol making archiving of an electronic document to multiple locations an atomic operation. | 02-10-2011 |
| 20110035219 | AUTOMATIC SPOKEN LANGUAGE IDENTIFICATION BASED ON PHONEME SEQUENCE PATTERNS - A language identification system that includes a universal phoneme decoder (UPD) is described. The UPD contains a universal phoneme set representing both 1) all phonemes occurring in the set of two or more spoken languages, and 2) captures phoneme correspondences across languages, such that a set of unique phoneme patterns and probabilities are calculated in order to identify a most likely phoneme occurring each time in the audio files in the set of two or more potential languages in which the UPD was trained on. Each statistical language models (SLM) uses the set of unique phoneme patterns created for each language in the set to distinguish between spoken human languages in the set of languages. The run-time language identifier module identifies a particular human language being spoken by utilizing the linguistic probabilities supplied by the one or more SLMs that are based on the set of unique phoneme patterns created for each language. | 02-10-2011 |
| 20100324901 | SPEECH RECOGNITION SYSTEM - Various methods and apparatus are described for a speech recognition system. In an embodiment, the statistical language model (SLM) provides probability estimates of how linguistically likely a sequence of linguistic items are to occur in that sequence based on an amount of times the sequence of linguistic items occurs in text and phrases in general use. The speech recognition decoder module requests a correction module for one or more corrected probability estimates P′(z|xy) of how likely a linguistic item z follows a given sequence of linguistic items x followed by y, where (x, y, and z) are three variable linguistic items supplied from the decoder module. The correction module is trained to linguistics of a specific domain, and is located in between the decoder module and the SLM in order to adapt the probability estimates supplied by the SLM to the specific domain when those probability estimates from the SLM significantly disagree with the linguistic probabilities in that domain. | 12-23-2010 |
| 20100223056 | VARIOUS APPARATUS AND METHODS FOR A SPEECH RECOGNITION SYSTEM - A method, apparatus, and system are described for a continuous speech recognition engine that includes a fine speech recognizer model, a coarse sound representation generator, and a coarse match generator. The fine speech recognizer model receives a time coded sequence of sound feature frames, applies a speech recognition process to the sound feature frames and determines at least a best guess at each recognizable word that corresponds to the sound feature frames. The coarse sound representation generator generates a coarse sound representation of the recognized word. The coarse match generator determines a likelihood of the coarse sound representation actually being the recognized word based on comparing the coarse sound representation of the recognized word to a database containing the known sound of that recognized word and assigns the likelihood as a robust confidence level parameter to that recognized word. | 09-02-2010 |
