Abstract: A method of querying a user to select from a list in a voice-activated command system is provided. The method includes generating command prompt phrases during which the user can select items on the list. The command prompt phrases include an item on the list and an index for another item on the list. In some embodiments, each command prompt phrase also includes a period of silence between item on the list and the index for another item on the list. If a user selecting barge-in is received during a particular command prompt phrase, the corresponding item on the list is selected.

Abstract: A speech recognition application including a recognition module configured to receive input utterances and an application module configured to select a recognition from the speech recognition module using output from a first iteration to select a recognition result for a second iteration. In one embodiment, the application module eliminates a previous rejected recognition result or results from the N-Best list for recognition. In another embodiment, the application module rescores N-Best entries based upon N-Best lists or information from another iteration. In another illustrated embodiment, the application module uses a limited grammar from a current N-Best list for subsequent recognition, for example for rerecognition using a recorded input from a previous iteration.

Abstract: A validation routine is integrated into or otherwise closely associated with a language model such as a context-free grammar. The validation routine receives recognition results from a speech recognizer that has used the corresponding grammar to form the recognized results. The validation routine operates upon the recognized results to ascertain legitimate recognition results based on the actual recognition results received rather than on acoustic and/or language model scores commonly used to provide confidence measures.

Abstract: A computer-implemented method is disclosed for creating a grammar to be processed by a speech recognition engine in the context of a voice-activated command system. The method includes receiving a database containing a plurality of terms and identifying a set of terms that are pronounced the same but spelled differently. The method also includes placing a single term within the grammar to represent the set of terms.

Abstract: A framework for easy and accurate transcription of speech data is provided. Utterances related to a single task are grouped together and processed using combinations of associated sets of recognition results and/or context information in a manner that allows the same transcription for a selected recognition result to be assigned to each of the utterances under consideration.

Abstract: A method is disclosed from constructing a grammar. The grammar is configured to be processed by a speech recognition engine in the context of a voice-activated command system. The method includes receiving a database containing a plurality of terms. From the plurality of terms, first and second terms are identified. The first and second terms are spelled differently but have a first pronunciation in common. One of the first and second terms also has a second pronunciation that is not inherent to the other of the first and second terms. The first and second pronunciations are placed within the grammar.

Abstract: A language model for a language processing system such as a speech recognition system is formed as a function of associated characters, word phrases and context cues. A method and apparatus for generating the training corpus used to train the language model and a system or module using such a language model is disclosed.

Abstract: A language model for a language processing system such as a speech recognition system is constructed from training corpus formed from associated characters, word phrases and context cues. A method and apparatus for generating the training corpus used to train the language model and a system or module using such a language model is disclosed.

Abstract: A process for collecting the identity of a telephone caller is disclosed. In one embodiment, a personalized Context Free Grammar (CFG) is created for each potential call recipient, and is configured to support identification of incoming callers utilizing voice recognition. Each CFG incorporates an indication of high probability callers and probability weights in each CFG are altered accordingly. When a recipient receives a call, the relevant CFG is applied in association with a voice recognition application 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 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.

Abstract: A speech recognition training system for Kanji-based languages is provided. The system loads a pronunciation aid 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) for the troubling ideograph is displayed.

Abstract: Controls are provided for a web server to generate client side markups that include recognition and/or audible prompting. The controls comprise elements of a dialog such as a prompt, answer, confirmation, command and validation. An application control provides a means to wrap common speech scenarios in one control.

Abstract: The speech recognizer includes a dictation language model providing a dictation model output indicative of a likely word sequence recognized based on an input utterance. A spelling language model provides a spelling model output indicative of a likely letter sequence recognized based on the input utterance. An acoustic model provides an acoustic model output indicative of a likely speech unit recognized based on the input utterances. A speech recognition component is configured to access the dictation language model, the spelling language model and the acoustic model. The speech recognition component weights the dictation model output and the spelling model output in calculating likely recognized speech based on the input utterance. The speech recognizer can also be configured to confine spelled speech to an active lexicon.

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 inputting ideograms into a computer system includes receiving phonetic information related to a desired ideogram to be entered and forming a candidate list of possible ideograms as a function of the phonetic information received. Stroke information, comprising one or more strokes in the desired ideogram, is received in order to obtain the desired ideogram from the candidate list.

Abstract: A language model for a language processing system such as a speech recognition system is formed as a function of associated characters, word phrases and context cues. A method and apparatus for generating the training corpus used to train the language model and a system or module using such a language model is disclosed.

Abstract: A method and system for dynamically selecting words for training a speech recognition system. The speech recognition system models each phoneme using a hidden Markov model and represents each word as a sequence of phonemes. The training system ranks each phoneme for each frame according to the probability that the corresponding codeword will be spoken as part of the phoneme. The training system collects spoken utterances for which the corresponding word is known. The training system then aligns the codewords of each utterance with the phoneme that it is recognized to be part of. The training system then calculates an average rank for each phoneme using the aligned codewords for the aligned frames. Finally, the training system selects words for training that contain phonemes with a low rank.