Abstract: Methods, computer programs, and systems for interfacing a user with a computer program, utilizing gaze detection and voice recognition, are provided. One method includes an operation for determining if a gaze of a user is directed towards a target associated with the computer program. The computer program is set to operate in a first state when the gaze is determined to be on the target, and set to operate in a second state when the gaze is determined to be away from the target. When operating in the first state, the computer program processes voice commands from the user, and, when operating in the second state, the computer program omits processing of voice commands.

Abstract: Methods of eye gaze tracking are provided using magnetized contact lenses tracked by magnetic sensors and/or reflecting contact lenses tracked by video-based sensors. Tracking information of contact lenses from magnetic sensors and video-based sensors may be used to improve eye tracking and/or combined with other sensor data to improve accuracy. Furthermore, reflective contact lenses improve blink detection while eye gaze tracking is otherwise unimpeded by magnetized contact lenses. Additionally, contact lenses may be adapted for viewing 3D information.

Abstract: An electronic device may be controlled using nerve analysis by measuring a nerve activity level for one or more body parts of a user of the device using one or more nerve sensors associated with the electronic device. A relationship can be determined between the user's one or more body parts and an intended interaction by the user with one or more components of the electronic device using each nerve activity level determined. A control input or reduced set of likely actions can be established for the electronic device based on the relationship determined.

Abstract: A tongue tracking interface apparatus for control of a computer program may include a mouthpiece configured to be worn over one or more teeth of a user of the computer program. The mouthpiece can include one or more sensors configured to determine one or more tongue orientation characteristics of the user. Other sensors such as microphones, pressure sensors, etc. located around the head, face, and neck, can also be used for determining tongue orientation characteristics.

Abstract: In syllable or vowel or phone boundary detection during speech, an auditory spectrum may be determined for an input window of sound and one or more multi-scale features may be extracted from the auditory spectrum. Each multi-scale feature can be extracted using a separate two-dimensional spectro-temporal receptive filter. One or more feature maps corresponding to the one or more multi-scale features can be generated and an auditory gist vector can be extracted from each of the one or more feature maps. A cumulative gist vector may be obtained through augmentation of each auditory gist vector extracted from the one or more feature maps. One or more syllable or vowel or phone boundaries in the input window of sound can be detected by mapping the cumulative gist vector to one or more syllable or vowel or phone boundary characteristics using a machine learning algorithm.

Abstract: A speech recognition engine is provided with an acoustic model and a layered grammar and dictionary library. The layered grammar and dictionary library includes a language and non-grammar layer that supplies types of rules a grammar definition layer can use and defines non-grammar the speech recognition engine should ignore. The layered grammar and dictionary library also includes a dictionary layer that defines phonetic transcriptions for word groups the speech recognition engine is meant to recognize when voice input is received. The layered grammar and dictionary library further includes a grammar definition layer that applies rules from the language and non-grammar layer to define combinations of word groups the speech recognition system is meant to recognize. Voice input is received at a speech recognition engine and is processed using the acoustic model and the layered grammar and dictionary library.

Abstract: A virtual object can be controlled using one or more touch interfaces. A location for a first touch input can be determined on a first touch interface. A location for a second touch input can be determined on a second touch interface. A three-dimensional segment can be generated using the location of the first touch input, the location of the second touch input, and a pre-determined spatial relationship between the first touch interface and the second touch interface. The virtual object can be manipulated using the three-dimensional segment in c) as a control input. The manipulated virtual object can be displayed on a display.

Abstract: A thermal imaging interface for control of a computer program may obtain one or more thermal infrared images of one or more objects with one or more thermographic cameras. The images may be analyzed to identify one or more characteristics of the objects. Such characteristics may be used as a control input in the computer program.

Abstract: A blow tracking user interface method and apparatus may detect an orientation of blowing of a user's breath and a magnitude of blowing of the user's breath. A blow vector may be generated from the orientation and magnitude of the blowing of the user's breath. The blow vector may be used as a control input in a computer program.

Abstract: An apparatus to improve robustness to environmental changes of a context dependent speech recognizer for an application, that includes a training database to store sounds for speech recognition training, a dictionary to store words supported by the speech recognizer, and a speech recognizer training module to train a set of one or more multiple state Hidden Markov Models (HMMs) with use of the training database and the dictionary. The speech recognizer training module performs a non-uniform state clustering process on each of the states of each HMM, which includes using a different non-uniform cluster threshold for at least some of the states of each HMM to more heavily cluster and correspondingly reduce a number of observation distributions for those of the states of each HMM that are less empirically affected by one or more contextual dependencies.

Abstract: Voice recognition methods and systems are disclosed. A voice signal is obtained for an utterance of a speaker. The speaker is categorized as a male, female, or child and the categorization is used as a basis for dynamically adjusting a maximum frequency fmax and a minimum frequency fmin of a filter bank used for processing the input utterance to produce an output. Corresponding gender or age specific acoustic models are used to perform voice recognition based on the filter bank output.

Abstract: Methods and apparatus for voice recognition are disclosed. A voice signal is obtained and two or more voice recognition analyses are performed on the voice signal. Each voice recognition analysis uses a filter bank defined by a different maximum frequency and a different minimum frequency and wherein each voice recognition analysis produces a recognition probability ri of recognition of one or more speech units, whereby there are two or more recognition probabilities ri. The maximum frequency and the minimum frequency may be adjusted every time speech is windowed and analyzed. A final recognition probability Pf is determined based on the two or more recognition probabilities ri.

Abstract: A speech recognition engine is provided with an acoustic model and a layered grammar and dictionary library. The layered grammar and dictionary library includes a language and non-grammar layer that supplies types of rules a grammar definition layer can use and defines non-grammar the speech recognition engine should ignore. The layered grammar and dictionary library also includes a dictionary layer that defines phonetic transcriptions for word groups the speech recognition engine is meant to recognize when voice input is received. The layered grammar and dictionary library further includes a grammar definition layer that applies rules from the language and non-grammar layer to define combinations of word groups the speech recognition system is meant to recognize. Voice input is received at a speech recognition engine and is processed using the acoustic model and the layered grammar and dictionary library.

Abstract: Use of runtime memory may be reduced in a data processing algorithm that uses one or more probability distribution functions. Each probability distribution function may be characterized by one or more uncompressed mean values and one or more variance values. The uncompressed mean and variance values may be represented by ?-bit floating point numbers, where ? is an integer greater than 1. The probability distribution functions are converted to compressed probability functions having compressed mean and/or variance values represented as ?-bit integers, where ? is less than ?, whereby the compressed mean and/or variance values occupy less memory space than the uncompressed mean and/or variance values. Portions of the data processing algorithm can be performed with the compressed mean and variance values.

Abstract: Methods for optimizing grammar structure for a set of phrases to be used in speech recognition during a computing event are provided. One method includes receiving a set of phrases, the set of phrases being relevant for the computing event and the set of phrases having a node and link structure. Also included is identifying redundant nodes by examining the node and link structures of each of the set of phrases so as to generate a single node for the redundant nodes. The method further includes examining the node and link structures to identify nodes that are capable of being vertically grouped and grouping the identified nodes to define vertical word groups. The method continues with fusing nodes of the set of phrases that are not vertically grouped into fused word groups. Wherein the vertical word groups and the fused word groups are linked to define an optimized grammar structure.

Abstract: Voice recognition methods and systems are disclosed. A voice signal is obtained for an utterance of a speaker. The speaker is categorized as a male, female, or child and the categorization is used as a basis for dynamically adjusting a maximum frequency fmax and a minimum frequency fmin of a filter bank used for processing the input utterance to produce an output. Corresponding gender or age specific acoustic models are used to perform voice recognition based on the filter bank output.

Abstract: Computer implemented speech processing is disclosed. First and second voice segments are extracted from first and second microphone signals originating from first and second microphones. The first and second voice segments correspond to a voice sound originating from a common source. An estimated source location is generated based on a relative energy of the first and second voice segments and/or a correlation of the first and second voice segments. A determination whether the voice segment is desired or undesired may be made based on the estimated source location.

Abstract: Computer implemented speech processing generates one or more pronunciations of an input word in a first language by a non-native speaker of the first language who is a native speaker of a second language. The input word is converted into one or more pronunciations. Each pronunciation includes one or more phonemes selected from a set of phonemes associated with the second language. Each pronunciation is associated with the input word in an entry in a computer database. Each pronunciation in the database is associated with information identifying a pronunciation language and/or a phoneme language.

Abstract: Speech processing is disclosed for an apparatus having a main processing unit, a memory unit, and one or more co-processors. Memory maintenance and voice recognition result retrievals upon execution are performed with a first main processor thread. Voice detection and initial feature extraction on the raw data are performed with a first co-processor. A second co-processor thread receives feature data derived for one or more features extracted by the first co-processor thread and information for locating probability density functions needed for probability computation by a speech recognition model and computes a probability that the one or more features correspond to a known sub-unit of speech using the probability density functions and the feature data. At least a portion of a path probability that a sequence of sub-units of speech correspond to a known speech unit is computed with a third co-processor thread.

Abstract: Voice recognition methods and systems are disclosed. A voice signal is obtained for an utterance of a speaker. A runtime pitch is determined from the voice signal for the utterance. The speaker is categorized based on the runtime pitch and one or more acoustic model parameters are adjusted based on a categorization of the speaker. The parameter adjustment may be performed at any instance of time during the recognition. A voice recognition analysis of the utterance is then performed based on the acoustic model.