A learning computer system may estimate unknown parameters and states of a stochastic or uncertain system having a probability structure. The system may include a data processing system that may include a hardware processor that has a configuration that: receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states.

A sound processing system includes a sound input device for providing a sound input, a sound output device for providing a sound output, and processing electronics including a processor and a memory, wherein the processing electronics is configured to receive a target sound input identifying a target sound, receive a rule input establishing a sound processing rule that references the target sound, receive a sound input from the sound input device, analyze the sound input for the target sound, process the sound input according to the sound processing rule in view of the analysis of the sound input, and provide a processed sound output to the sound output device.

Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Disclosed herein are system, apparatus, article of manufacture, method, and computer program product embodiments for adapting an automated speech recognition system to provide more accurate suggestions to voice queries involving media content including recently created or recently available content. An example computer-implemented method includes transcribing the voice query, identifying respective components of the query such as the media content being requested and the action to be performed, and generating fuzzy candidates that potentially match the media content based on phonetic representations of the identified components. Phonetic representations of domain specific candidates are stored in a domain entities index and is continuously updated with new entries so as to maintain the accuracy of the speech recognition of voice queries for recently created or recently available content.

Systems and processes are disclosed for discovering trending terms in automatic speech recognition. Candidate terms (e.g., words, phrases, etc.) not yet found in a speech recognizer vocabulary or having low language model probability can be identified based on trending usage in a variety of electronic data sources (e.g., social network feeds, news sources, search queries, etc.). When candidate terms are identified, archives of live or recent speech traffic can be searched to determine whether users are uttering the candidate terms in dictation or speech requests. Such searching can be done using open vocabulary spoken term detection to find phonetic matches in the audio archives. As the candidate terms are found in the speech traffic, notifications can be generated that identify the candidate terms, provide relevant usage statistics, identify the context in which the terms are used, and the like.

Systems and processes are disclosed for discovering trending terms in automatic speech recognition. Candidate terms (e.g., words, phrases, etc.) not yet found in a speech recognizer vocabulary or having low language model probability can be identified based on trending usage in a variety of electronic data sources (e.g., social network feeds, news sources, search queries, etc.). When candidate terms are identified, archives of live or recent speech traffic can be searched to determine whether users are uttering the candidate terms in dictation or speech requests. Such searching can be done using open vocabulary spoken term detection to find phonetic matches in the audio archives. As the candidate terms are found in the speech traffic, notifications can be generated that identify the candidate terms, provide relevant usage statistics, identify the context in which the terms are used, and the like.

A token is extracted from a Natural Language input. A phonetic pattern is computed corresponding to the token, the phonetic pattern including a sound pattern that represents a part of the token when the token is spoken. New data is created from data of the phonetic pattern, the new data including a syllable sequence corresponding to the phonetic pattern. A state of a data storage device is changed by storing the new data in a matrix of syllable sequences corresponding to the token. An option is selected that corresponds to the token by executing a fuzzy matching algorithm using a processor and a memory, the selecting of the option is based on a syllable sequence in the matrix.

This disclosure describes a solution to identify that a meaningful event has occurred in a person's life. Once identified, data and content related to the event can be collected and stored in a database. This data and content can be used to offer future virtual reality (VR) experiences and content. A person can be equipped with a smart device such as a smartphone, smart watch, or other wearable that can further be equipped with a tracking app that collects data from these devices and from other sources, for instance, over the internet. The tracking app can continuously collect such data and use various algorithms to make a determination as to whether the person is experiencing a meaningful life event.

This disclosure describes a solution to identify that a meaningful event has occurred in a person's life. Once identified, data and content related to the event can be collected and stored in a database. This data and content can be used to offer future virtual reality (VR) experiences and content. A person can be equipped with a smart device such as a smartphone, smart watch, or other wearable that can further be equipped with a tracking app that collects data from these devices and from other sources, for instance, over the internet. The tracking app can continuously collect such data and use various algorithms to make a determination as to whether the person is experiencing a meaningful life event.

This disclosure describes a solution to identify that a meaningful event has occurred in a person's life. Once identified, data and content related to the event can be collected and stored in a database. This data and content can be used to offer future virtual reality (VR) experiences and content. A person can be equipped with a smart device such as a smartphone, smart watch, or other wearable that can further be equipped with a tracking app that collects data from these devices and from other sources, for instance, over the internet. The tracking app can continuously collect such data and use various algorithms to make a determination as to whether the person is experiencing a meaningful life event.