Abstract:
A process for collecting the identity of a telephone caller is disclosed. In one embodiment, a personalized Context Free Grammar (CFG)(212) is created for each potential call recipient (210), and is configured to support identification of incoming callers utilizing voice recognition. Each CFG (212) incorporates an indication of high probability callers and probability weights in each CFG are altered accordingly. When a recipient (210) receives a call (202), the relevant CFG (212) is applied in association with a voice recognition application (222) to enable at least a preliminary identification of the caller. In accordance with another embodiment, the caller confirms identifications. In accordance with one embodiment, standard caller-ID functionality (224) is utilized if possible at least to assist in the caller identification process. In accordance with still another embodiment, voice recognition enhanced caller identification is utilized to provide intelligent call routing functionality (226).
Abstract:
A process for collecting the identity of a telephone caller is disclosed. In one embodiment, a personalized Context Free Grammar (CFG)(212) is created for each potential call recipient (210), and is configured to support identification of incoming callers utilizing voice recognition. Each CFG (212) incorporates an indication of high probability callers and probability weights in each CFG are altered accordingly. When a recipient (210) receives a call (202), the relevant CFG (212) is applied in association with a voice recognition application (222) to enable at least a preliminary identification of the caller. In accordance with another embodiment, the caller confirms identifications. In accordance with one embodiment, standard caller-ID functionality (224) is utilized if possible at least to assist in the caller identification process. In accordance with still another embodiment, voice recognition enhanced caller identification is utilized to provide intelligent call routing functionality (226).
Abstract:
A method for responding to an electronic mail message with a limited input device such as a phone includes audibly rendering the question and a set of proposed answers typically provided in the electronic mail message by the sender of the electronic mail message. A language model indicative of the proposed answers is provided to a speech recognizer. The response from the user is obtained and converted to a textual response using the speech recognizer and language model. A second electronic e-mail message is then sent back to the sender. The second electronic mail message includes the textual response.
Abstract:
A method for responding to an electronic mail message with a limited input device such as a phone includes audibly rendering the question and a set of proposed answers typically provided in the electronic mail message by the sender of the electronic mail message. A language model indicative of the proposed answers is provided to a speech recognizer. The response from the user is obtained and converted to a textual response using the speech recognizer and language model. A second electronic e-mail message is then sent back to the sender. The second electronic mail message includes the textual response.
Abstract:
A method and system to generate a grammar adapted for use by a speech recognizer includes receiving a representation of an alphanumeric expression. For instance, the representation can take the form of a regular expression or a mask. The grammar is generated based on the representation.
Abstract:
A speech recognition training system for Kanji-based languages is provided. The system loads a pronunciation aid (232) for each and every ideograph in the training speech, but does not in fact display an ideograph until the training system recognizes a pronunciation difficulty. Once a pronunciation difficulty is identified, the associated pronunciation aid (rubi) (232) for the troubling ideograph is displayed.