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: A method and apparatus determine the likelihood of a sequence of words based in part on a segment model. The segment model includes trajectory expressions formed as the product of a polynomial matrix and a generation matrix. The likelihood of the sequence of words is based in part on a segment probability derived by subtracting the trajectory expressions from a feature vector matrix that contains a sequence of feature vectors for a segment of speech. Aspects of the method and apparatus also include training the segment model using such a segment probability.

Abstract: Web server controls are provided for generating client side markups with recognition and/or audible prompting. Three approaches are disclosed for implementation of the controls.

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 question, answer, confirmation, command or statement. A module forms a dialog by making use of the information carried in the controls.

Abstract: A method and apparatus is provided for identifying patterns from a series of feature vectors representing a time-varying signal. The method and apparatus use both a frame-based model and a segment model in a unified framework. The frame-based model determines the probability of an individual feature vector given a frame state. The segment model determines the probability of sub-sequences of feature vectors given a single segment state. The probabilities from the frame-based model and the segment model are then combined to form a single path score that is indicative of the probability of a sequence of patterns. Another aspect of the invention is the use of a frame-based model and a segment model to segment feature vectors during model training. Under this aspect of the invention, the frame-based model and the segment model are used together to identify probabilities associated with different segmentations.

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: Web server controls are provided for generating client side markups with recognition and/or audible prompting. Three approaches are disclosed for implementation of the controls.

Abstract: A speech recognition method and system utilize an acoustic model that is capable of providing probabilities for both a large acoustic unit and an acoustic sub-unit. Each of these probabilities describes the likelihood of a set of feature vectors from a series of feature vectors representing a speech signal. The large acoustic unit is formed from a plurality of acoustic sub-units. At least one sub-unit probability and at least on large unit probability from the acoustic model are used by a decoder to generate a score for a sequence of hypothesized words. When combined, the acoustic sub-units associated with all of the sub-unit probabilities used to determine the score span fewer than all of the feature vectors in the series of feature vectors. An overlapping decoding technique is also provided.

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 question, answer, confirmation, command or statement. A module forms a dialog by making use of the information carried in the controls.

Abstract: Predictive keyboards, such as predictive soft keyboards, are disclosed. In one embodiment, a computer-implemented method predicts at least one key to be entered next within a sequence of keys. The method displays a soft keyboard where the predicted keys are displayed on the soft keyboard differently than the other keys on the keyboard. For example, the predicted keys may be larger in size on the soft keyboard as compared to the other keys. This makes the predicted keys more easily typed by a user as compared to the other keys.

Abstract: A method and system of performing confidence measure in a speech recognition system includes receiving an utterance of input speech and creating a near-miss pattern or a near-miss list of possible word entries for the utterance. Each word entry includes an associated value of probability that the utterance corresponds to the word entry. The near-miss list of possible word entries is compared with corresponding stored near-miss confidence templates. Each word in the vocabulary (or keyword list) of near-miss confidence template, which includes a list of word entries and each word entry in each list includes an associated value. Confidence measure for a particular hypothesis word is performed based on the comparison of the values in the near-miss list of possible word entries with the values of the corresponding near-miss confidence template.

Abstract: A server/client system for processing data includes a network having a web server with information accessible remotely. A client device includes a microphone and a rendering component such as a speaker or display. The client device is configured to obtain the information from the web server and record input data associated with fields contained in the information. The client device is adapted to send the input data to a remote location with an indication of a grammar to use for recognition. A recognition server receives the input data and the indication of the grammar. The recognition server returns data indicative of what was recognized to at least one of the client and the web server.

Abstract: A computer implemented system and method of proofreading text in a computer system includes receiving text from a user into a text editing module. At least a portion of the text is converted to an audio signal upon the detection of an indicator, the indicator defining a boundary in the text by either being embodied therein or comprising delays in receiving text. The audio signal is played through a speaker to the user to provide feedback.

Abstract: A markup language for execution on a client device in a client/server system includes extensions for recognition.

Abstract: A markup language for execution on a client device in a client/server system includes extensions for recognition.

Abstract: A markup language for execution on a client device in a client/server system includes instructions to unify at least one of recognition-related events, GUI events and telephony events on non-display, voice input based client device and a multimodal based client for a web server interacting with each of the client devices. A recognition server for receiving data indicative of inputted data provided to a client device and an indication of a grammar to use for recognition is also provided.

Abstract: A computer implemented system and method of proofreading text in a computer system includes receiving text from a user into a text editing module. At least a portion of the text is converted to an audio signal. The audio signal is played through a speaker to the user to provide feedback.

Abstract: A method and system of performing confidence measure in a speech recognition system includes receiving an utterance of input speech and creating a near-miss pattern or a near-miss list of possible word entries for the utterance. Each word entry includes an associated value of probability that the utterance corresponds to the word entry. The near-miss list of possible word entries is compared with corresponding stored near-miss confidence templates. Each word in the vocabulary (or keyword list) of near-miss confidence template, which includes a list of word entries and each word entry in each list includes an associated value. Confidence measure for a particular hypothesis word is performed based on the comparison of the values in the near-miss list of possible word entries with the values of the corresponding near-miss confidence template.

Abstract: A text-to-speech system includes a storage device for storing a clustered set of context-dependent phoneme-based units of a target speaker. In one embodiment, decision trees are used wherein each decision tree based context-dependent phoneme-based unit is arranged based on context of at least one immediately preceding and succeeding phoneme. At least one of the context-dependent phoneme-based units represents other non-stored context-dependent phoneme units of similar sound due to similar contexts. A text analyzer obtains a string of phonetic symbols representative of text to be converted to speech. A concatenation module selects stored decision tree based context-dependent phoneme-based units from the set decision tree based context-dependent phoneme-based units based on the context of the phonetic symbols and synthesizes the selected phoneme-based units to generate speech corresponding to the text.

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.