Abstract: A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample.

Abstract: A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample.

Abstract: A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample.

Abstract: A method includes obtaining a plurality of training data sets each associated with a respective native language and includes a plurality of respective training data samples. For each respective training data sample of each training data set in the respective native language, the method includes transliterating the corresponding transcription in the respective native script into corresponding transliterated text representing the respective native language of the corresponding audio in a target script and associating the corresponding transliterated text in the target script with the corresponding audio in the respective native language to generate a respective normalized training data sample.

Abstract: Some implementations are directed to translating chatspeak to a normalized form, where the chatspeak is included in natural language input formulated by a user via a user interface input device of a computing device—such as input provided by the user to an automated assistant. The normalized form of the chatspeak may be utilized by the automated assistant in determining reply content that is responsive to the natural language input, and that reply content may be presented to the user via one or more user interface output devices of the computing device of the user. Some implementations are additionally and/or alternatively directed to providing, for presentation to a user, natural language output that includes chatspeak in lieu of a normalized form of the chatspeak, based at least in part on a “chatspeak measure” that is determined based on past usage of chatspeak by the user and/or by additional users.

Abstract: Some implementations are directed to translating chatspeak to a normalized form, where the chatspeak is included in natural language input formulated by a user via a user interface input device of a computing device—such as input provided by the user to an automated assistant. The normalized form of the chatspeak may be utilized by the automated assistant in determining reply content that is responsive to the natural language input, and that reply content may be presented to the user via one or more user interface output devices of the computing device of the user. Some implementations are additionally and/or alternatively directed to providing, for presentation to a user, natural language output that includes chatspeak in lieu of a normalized form of the chatspeak, based at least in part on a “chatspeak measure” that is determined based on past usage of chatspeak by the user and/or by additional users.

Abstract: Some implementations are directed to translating chatspeak to a normalized form, where the chatspeak is included in natural language input formulated by a user via a user interface input device of a computing device—such as input provided by the user to an automated assistant. The normalized form of the chatspeak may be utilized by the automated assistant in determining reply content that is responsive to the natural language input, and that reply content may be presented to the user via one or more user interface output devices of the computing device of the user. Some implementations are additionally and/or alternatively directed to providing, for presentation to a user, natural language output that includes chatspeak in lieu of a normalized form of the chatspeak, based at least in part on a “chatspeak measure” that is determined based on past usage of chatspeak by the user and/or by additional users.

Abstract: Some implementations are directed to translating chatspeak to a normalized form, where the chatspeak is included in natural language input formulated by a user via a user interface input device of a computing device—such as input provided by the user to an automated assistant. The normalized form of the chatspeak may be utilized by the automated assistant in determining reply content that is responsive to the natural language input, and that reply content may be presented to the user via one or more user interface output devices of the computing device of the user. Some implementations are additionally and/or alternatively directed to providing, for presentation to a user, natural language output that includes chatspeak in lieu of a normalized form of the chatspeak, based at least in part on a “chatspeak measure” that is determined based on past usage of chatspeak by the user and/or by additional users.

Abstract: Embodiments of the disclosed technology provide reliable and fast communication of a human through a direct brain interface which detects the intent of the user. An embodiment of the disclosed technology comprises a system and method in which least one sequence of a plurality of stimuli is presented to an individual (using appropriate sensory modalities), and the time course of at least one measurable response to the sequence(s) is used to select at least one stimulus from the sequence(s). In an embodiment, the sequence(s) may be dynamically altered based on previously selected stimuli and/or on estimated probability distributions over the stimuli. In an embodiment, such dynamic alteration may be based on predictive models of appropriate sequence generation mechanisms, such as an adaptive or static sequence model.

Abstract: Systems and methods relate to generating a language model for use in, for example, a spoken dialog system or some other application. The method comprises building a class-based language model, generating at least one sequence network and replacing class labels in the class-based language model with the at least one sequence network. In this manner, placeholders or tokens associated with classes can be inserted into the models at training time and word/phone networks can be built based on meta-data information at test time. Finally, the placeholder token can be replaced with the word/phone networks at run time to improve recognition of difficult words such as proper names.

Abstract: Disclosed are systems and methods for providing a spoken dialog system using meta-data to build language models to improve speech processing. Meta-data is generally defined as data outside received speech; for example, meta-data may be a customer profile having a name, address and purchase history of a caller to a spoken dialog system. The method comprises building tree clusters from meta-data and estimating a language model using the built tree clusters. The language model may be used by various modules in the spoken dialog system, such as the automatic speech recognition module and/or the dialog management module. Building the tree clusters from the meta-data may involve generating projections from the meta-data and further may comprise computing counts as a result of unigram tree clustering and then building both unigram trees and higher-order trees from the meta-data as well as computing node distances within the built trees that are used for estimating the language model.