Abstract: A system and method dispense medication through a smart pill that holds at least one medication and a mobile device capable of communicating wirelessly with the smart pill. The mobile device includes a user interface for communicating with a patient and at least one application program that includes program instructions for measuring an aspect of a patient's physiology. A processor on the mobile device is configured to execute a program for monitoring and communicating with the smart pill, causing at least one of the applications to execute and test the patient, and on the basis of the outcome of the testing, issue a signal for timing and amount of medication release from the smart pill to the patient. The application programs may include a cognitive test, an eye test, a balance test, and a reaction test which may use the display, camera, speaker and microphone of the mobile device.

Abstract: Deep Neural Networks (DNN) are time shifted relative to one another and trained. The time-shifted networks may then be combined to improve recognition accuracy. The approach is based on an automatic speech recognition (ASR) system using DNN and using time shifted features. Initially, a regular ASR model is trained to produce a first trained DNN. Then a top layer (e.g., SoftMax layer) and the last hidden layer (e.g., Sigmoid) are fine-tuned with same data set but with a feature window left- and right-shifted to create respective second and third left-shifted and right-shifted DNNs. From these three DNN networks, four combination networks may be generated: left- and right-shifted, left-shifted and centered, centered and right-shifted, and left-shifted, centered, and right-shifted. The centered networks are used to perform the initial (first-pass) ASR. Then the other six networks are used to perform rescoring.

Abstract: A hybrid frame, phone, diphone, morpheme, and word-level Deep Neural Networks (DNN) in model training and applications-is based on training a regular ASR system, which can be based on Gaussian Mixture Models (GMM) or DNN. All the training data (in the format of features) are aligned with the transcripts in terms of phonemes and words with the timing information and new features are formed in terms of phonemes, diphones, morphemes, and up to words. Regular ASR produces a result lattice with timing information for each word. A feature is then extracted and sent to the word-level DNN for scoring Phoneme features are sent to corresponding DNNs for training. Scores are combined to form the word level scores, a rescored lattice and a new recognition result.

Abstract: A mobile device is programmed with an application that uses the mobile device's camera, accelerometer and microphone to enable a parent, coach or player to use it as a tool to diagnose a concussion. The tool may diagnose concussion on the basis of one or multiple factors that are scored, for example the player's balance, eye movement, speech responses to questions, button pressing response time, and other information about the location of the impact. A mobile device may be equipped with speech recognition and voice prompting to enable a concussion examination of a player to be administered by another player or coach to the injured player without significant effort by the injured player or helper. Each test may be scored, by itself or against one or more baselines for the injured player to develop an overall score and likelihood of a concussion. When the coach thinks there is a concussion, he/she can use the application to help find a doctor.

Abstract: Phoneme images are created for keywords and audio files. The keyword images and audio file images are used to identify keywords within the audio file when the phoneme images match. Confidence scores may be determined corresponding to the match. Audio around the keywords may be stored and processed with an automatic speech recognition (ASR) program to verify the keyword match and provide textual and audio context to where the keyword appears within speech.

Abstract: Deep Neural Networks (DNN) are time shifted relative to one another and trained. The time-shifted networks may then be combined to improve recognition accuracy. The approach is based on an automatic speech recognition (ASR) system using DNN and using time shifted features. Initially, a regular ASR model is trained to produce a first trained DNN. Then a top layer (e.g., SoftMax layer) and the last hidden layer (e.g., Sigmoid) are fine-tuned with same data set but with a feature window left- and right-shifted to create respective second and third left-shifted and right-shifted DNNs. From these three DNN networks, four combination networks may be generated: left- and right-shifted, left-shifted and centered, centered and right-shifted, and left-shifted, centered, and right-shifted. The centered networks are used to perform the initial (first-pass) ASR. Then the other six networks are used to perform rescoring.

Abstract: An approach of hybrid frame, phone, diphone, morpheme, and word-level Deep Neural Networks (DNN) in model training and applications is described. The approach can be applied to many applications. The approach is based on a regular ASR system, which can be based on Gaussian Mixture Models (GMM) or DNN. In the first step, a regular ASR model is trained. All the training data (in the format of features) are aligned with the transcripts in terms of phonemes and words with the timing information. Feature normalization can be applied for these new features. Based on the alignment timing information, new features are formed in terms of phonemes, diphones, morphemes, and up to words. A first pass regular speech recognition is performed, and the result lattice is produced. In the lattice, there is the timing information for each word. A feature is then extracted and sent to the word-level DNN for scoring.

Abstract: Phoneme images are created for keywords and audio files. The keyword images and audio file images are used to identify keywords within the audio file when the phoneme images match. Confidence scores may be determined corresponding to the match. Audio around the keywords may be stored and processed with an automatic speech recognition (ASR) program to verify the keyword match and provide textual and audio context to where the keyword appears within speech.