Abstract: This specification describes technologies relating to system, methods, and articles for updating a speech recognition dictionary based on, at least in part, both search query and market data metrics. In general, one innovative aspect of the subject matter described in this specification can be embodied in a method comprising (i) identifying a candidate term for possible inclusion in a speech recognition dictionary, (ii) identifying at least one search query metric associated with the identified candidate term, (iii) identifying at least one market data metric associated with the identified candidate term, and (iv) generating a candidate term score for the identified candidate term based, at least in part, on a weighted combination of the at least one identified search query metric and the at least one identified market data metric.

Abstract: A method, computer program product and system are provided. The method includes the steps of: providing output text and a confidence value and displaying the output text. The output text includes text in a second language produced from machine translation performed on input text in a first language. The confidence value corresponds to an indication of reliability of the output text. The display of the output text includes a display attribute corresponding to the confidence value of the output text.

Abstract: A method, computer program product and system are provided. The method includes the steps of: providing output text and a confidence value and displaying the output text. The output text includes text in a second language produced from machine translation performed on input text in a first language. The confidence value corresponds to an indication of reliability of the output text. The display of the output text includes a display attribute corresponding to the confidence value of the output text.

Abstract: Apparatus, and associated methods, for transforming an image. The apparatus includes an aiming device to be aimed at an original stationary image, a transforming device which transforms the image into a revised image, and a displaying device which displays the revised image. The revised image may be related linguistically or pictographically to the original stationary image, or the revised image includes a label assigned to at least one pictographic property of the original stationary image. Uses of the apparatus include, but are not limited to, translating text from one language to another, simplifying text within the same language and labelling text with one or more pictographic icons. The apparatus can include glasses or goggles that are worn by a user and in which an image, corresponding to the revised image, is projected or displayed on at least one lens area.

Abstract: In an illustrative embodiment, a speaker model is generated for each of a number of speakers from which speech samples have been obtained. Each speaker model contains a collection of distributions of audio feature data derived from the speech sample of the associated speaker. A hierarchical speaker model tree is created by merging similar speaker models on a layer by layer basis. Each time two or more speaker models are merged, a corresponding parent speaker model is created in the next higher layer of the tree. The tree is useful in applications such as speaker verification and speaker identification.

Abstract: A method for generating a hierarchical speaker model tree. In an illustrative embodiment, a speaker model is generated for each of a number of speakers from which speech samples have been obtained. Each speaker model contains a collection of distributions of audio feature data derived from the speech sample of the associated speaker. The hierarchical speaker model tree is created by merging similar speaker models on a layer by layer basis. Each time two or more speaker models are merged, a corresponding parent speaker model is created in the next higher layer of the tree. The tree is useful in applications such as speaker verification and speaker identification. A speaker verification method is disclosed in which a claimed ID from a claimant is received, where the claimed ID represents a speaker corresponding to a particular one of the speaker models. A cohort set of similar speaker models associated with the particular speaker model is established.

Abstract: Apparatus, and associated methods, for transforming an image. The apparatus includes an aiming device to be aimed at an original stationary image, a transforming device which transforms the image into a revised image, and a displaying device which displays the revised image. The revised image may be related linguistically or pictographically to the original stationary image, or the revised image includes a label assigned to at least one pictographic property of the original stationary image. Uses of the apparatus include, but are not limited to, translating text from one language to another, simplifying text within the same language and labelling text with one or more pictographic icons. The apparatus can include glasses or goggles that are worn by a user and in which an image, corresponding to the revised image, is projected or displayed on at least one lens area.

Abstract: A method for unsupervised environmental normalization for speaker verification using hierarchical clustering is disclosed. Training data (speech samples) are taken from T enrolled (registered) speakers over any one of M channels, e.g., different microphones, communication links, etc. For each speaker, a speaker model is generated, each containing a collection of distributions of audio feature data derived from the speech sample of that speaker. A hierarchical speaker model tree is created, e.g., by merging similar speaker models on a layer by layer basis. Each speaker is also grouped into a cohort of similar speakers. For each cohort, one or more complementary speaker models are generated by merging speaker models outside that cohort. When training data from a new speaker to be enrolled is received over a new channel, the speaker model tree as well as the complementary models are updated.

Abstract: A method for computing a distance between collections of distributions or finite mixture models of features. Data is processed so as to define at least first and second collections of distributions of features. For each distribution of the first collection, the distance to each distribution of the second collection is measured to determine which distribution of the second collection is the closest (most similar). The same procedure is performed for the distributions of the second collection. Based on the closest distance measures, a final distance is computed representing the distance between the first and second collections. This final distance may be a weighted sum of the closest distances. The distance measure may be used in a number of applications such as [speaker classification,] speaker recognition and audio segmentation.

Abstract: Machine recognition of isolated word utterances is carried out by applying time-indexed feature vectors to binary decision tree classifiers. A respective classifier is provided for each target word in the vocabulary of words to be recognized. Determinants for the nodes in the classifier tree structure are formed, during a training process, as hyper-planes which perform a "mean split" between the centroids of target word and non-target word classes of feature vectors assigned to the respective nodes. The process of training the machine recognition system is facilitated by storing node-assignment data in association with training data vectors. The assignment of training data vectors to sub-nodes proceeds on a level-by-level basis in the tree structure.