Abstract: A multi-lingual time-synchronized translation system and method provide automatic time-synchronized spoken translations of spoken phrases. The multi-lingual time-synchronized translation system includes a phrase-spotting mechanism, optionally, a language understanding mechanism, a translation mechanism, a speech output mechanism and an event measuring mechanism. The phrase-spotting mechanism identifies a spoken phrase from a restricted domain of phrases. The language understanding mechanism, if present, maps the identified phrase onto a small set of formal phrases. The translation mechanism maps the formal phrase onto a well-formed phrase in one or more target languages. The speech output mechanism produces high-quality output speech using the output of the event measuring mechanism for time synchronization. The event-measuring mechanism measures the duration of various key events in the source phrase.

Abstract: A programmable automatic call and data transfer processing system which automatically processes incoming telephone calls, facsimiles and e-mails based on the identity of the caller or author, the subject matter of the message or request, and/or the time of day, which includes: a central server for automatically answering an incoming call and collecting voice data of a caller; a speaker recognition module connected to the server for identifying the caller or author; a switching module responsive to the speaker recognition module for processing the call or message in accordance with a pre-programmed procedure based on the identification of the caller or author; and a programming interface for programming the server, speaker recognizer module and the switching module. The system is programmed by the user to so as to process incoming telephone calls or e-mail and facsimile messages based on the identity of the caller or author, subject matter and content of the message and the time of day.

Abstract: The invention proposes using statistical methods to do natural language understanding. The key notion is that there are "strings" of words in the natural language, that correspond to a single semantic concept. One can then define an alignment between an entire semantic meaning (consisting of a set of semantic concepts), and the English. This is modeled using P(E,A.vertline.S). One can model p(S) separately. This allows each parameter to be modeled using many different statistical models.