Abstract: The system provides a synthesized speech response to a voice input, based on the prosodic character of the voice input. The system receives the voice input and calculates at least one prosodic metric of the voice input. The at least one prosodic metric can be associated with a word, phrase, grouping thereof, or the entire voice input. The system also determines a response to the voice input, which may include the sequence of words that form the response. The system generates the synthesized speech response, by determining prosodic characteristics based on the response, and on the prosodic character of the voice input. The system outputs the synthesized speech response, which includes a more natural, relevant, or both answer to the call of the voice input. The prosodic character of the voice input and/or response may include pitch, note, duration, prominence, timbre, rate, and rhythm, for example.

Abstract: The system trains a model to provide information used to provide a synthesized speech response to a voice input. The model takes as input prosodic information that may include pitch, note, duration, prominence, timbre, rate, and rhythm, for example. The system receives a plurality of voice inputs, each associated with prosodic metric, as well as a plurality of responses, each also associated with prosodic metrics. The system trains the model based on the plurality of voice inputs, the plurality of responses, the prosodic metrics of the voice inputs, and the prosodic metrics of the responses such that the model outputs information used to generate the response. The model may also take as input user profile information, emotion metrics, and transition information to generate output. The output of the training model may be used by the system to provide synthesized speech responses having relevant prosodic character to received voice inputs.

Abstract: A system for generating annotations is configured to generate annotations having a format similar to a title and tailored to a user profile. The system identifies a media content item, and associated metadata, and determines a title corresponding to the media content item. The system generates a title template based on the title and based on metadata associated with the media content item. Based on the title template and user profile information, the system generates an annotation, which is linked to the media content item. The system may generate more than one annotation, and then select among the annotations for the most appropriate annotation for a given user. The annotation may include keywords or entities that are included in, linked to, or otherwise associated with the user profile information. The system outputs, or generates for output, a display that includes a representation of the media content item and the annotation.

Abstract: A natural language search query is processed to determine whether it has a grammatical structure. If so, a number of terms are identified in the natural language search query and each term is associated with a part of speech. Based on the grammatical structure, a type of query is determined, and the natural language search query is interpreted in the context of the query type based on the parts of speech of each term in the natural language search query.

Abstract: Systems and methods are described herein for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed, including, for example, processing each word of the query. Each term is associated with a respective part of speech, and a frequency of occurrence of each term in content metadata is determined. A relevance of each term is then determined based on its respective part of speech and frequency. The natural language search query is then interpreted based on the importance or relevance of each term.

Abstract: A natural language search is interpreted through use of machine learning, such as using one or more neural networks. After identifying a number of terms in the natural language search query, a vector is generated for each term describing a relationship between each term and a plurality of other terms. Each vector is then input into a trained neural network that generates an output based on the input vectors. The natural language search query is then interpreted based on the output of the neural network.

Abstract: A frequency of occurrence for each term in a training data set is determined in relation to the entire training data set. A relational data structure is generated that associates each term in the training data with its respective frequency. Any term that has a frequency below a threshold frequency is then added to a list of relevant words. When a natural language search query is received, a plurality of terms in the natural language search query are identified and compared with the list of relevant words. If any term of the natural language search query is included in the relevant words list, that term is identified as a keyword. The natural language search query is then interpreted based on any identified keywords.

Abstract: Systems and methods for training a classifier binary model of a natural language understanding (NLU) system are disclosed herein. A determination is made as to whether a text string, with a content entity, includes an obsequious expression. In response to determining the text string includes an obsequious expression, a determination is made as to whether the obsequious expression describes the content entity. The model is trained based on a determination of at least one of: an absence of an obsequious expression in response to determining the obsequious expression describes the content entity; a presence of an obsequious expression in response to determining the obsequious expression describes the content entity; an absence of an obsequious expression in response to determining the obsequious expression does not describe the content entity, and a presence of an obsequious expression in response to determining the obsequious expression does not describe the content entity.

Abstract: Systems and methods for generating a query using a trained natural language model of a natural language understanding (NLU) system are disclosed herein. A text string including at least a content entity is received. A determination is made as to whether the text string includes an obsequious expression. In response to determining the text string includes an obsequious expression, a determination is made as to whether the obsequious expression describes the content entity. In response to determining whether the obsequious expression describes the content entity, the query is generated. In response to determining the obsequious expression describes the content entity, the content entity and the obsequious expression are included in the query and in response to determining the obsequious expression does not describe the content entity, the content entity is included in the query and the obsequious expression is excluded from the query.

Abstract: A system for generating annotations is configured to generate annotations having a format similar to a title and tailored to a user profile. The system identifies a media content item, and associated metadata, and determines a title corresponding to the media content item. The system generates a title template based on the title and based on metadata associated with the media content item. Based on the title template and user profile information, the system generates an annotation, which is linked to the media content item. The system may generate more than one annotation, and then select among the annotations for the most appropriate annotation for a given user. The annotation may include keywords or entities that are included in, linked to, or otherwise associated with the user profile information. The system outputs, or generates for output, a display that includes a representation of the media content item and the annotation.

Abstract: Systems and methods for correcting recognition errors in speech recognition systems are disclosed herein. Natural conversational variations are identified to determine whether a query intends to correct a speech recognition error or whether the query is a new command. When the query intends to correct a speech recognition error, the system identifies a location of the error and performs the correction. The corrected query can be presented to the user or be acted upon as a command for the system.

Abstract: Systems and methods for determining to perform an action of a query using a trained natural language model of a natural language understanding (NLU) system are disclosed herein. A text string corresponding to a prescribed action includes at least a content entity is received. A determination is made as to whether the text string corresponds to an audio input of a first group. In response to determining the text string corresponds to an audio input of a first group, a determination is made as to whether the text string includes an obsequious expression. In response to determining the text string corresponds to an audio input of a first group and in response to determining the text string includes an obsequious expression, a determination is made to perform the prescribed action.

Abstract: Systems and methods for training a natural language model of a natural language understanding (NLU) system are disclosed herein. A text string including at least a content entity is received. A determination is made as to whether the text string includes an obsequious expression. In response to determining the text string includes an obsequious expression, a determination is made as to whether the obsequious expression describes the content entity. A query is forwarded in response to determining the text string includes an obsequious expression and in determining the obsequious expression describes the content entity. In response to determining the obsequious expression describes the content entity, the query includes the obsequious expression and in response to determining the obsequious expression does not describe the content entity, the query does not include the obsequious expression.

Abstract: A system for managing media content annotations is configured to generate annotations having a format similar to a title and tailored to a user profile. The system identifies a media content item and identifies a user entity. The system selects from among a plurality of annotations linked to the media content item and stored in metadata. For example, the system may generate more than one annotation, generate links between each annotation and user profile information, and then select among the annotations for the most appropriate annotation for a given user. The annotation may include keywords or entities that are included in, linked to, or otherwise associated with the user profile information. The system outputs, or generates for output, a display that includes a representation of the media content item and the selected annotation.

Abstract: An exemplary automatic speech recognition (ASR) system may receive an audio input including a segment of speech. The segment of speech may be independently processed by general ASR and domain-specific ASR to generate multiple ASR results. A selection between the multiple ASR results may be performed based on respective confidence levels for the general ASR and domain-specific ASR. As incremental ASR is performed, a composite result may be generated based on general ASR and domain-specific ASR.

Abstract: Systems and methods are disclosed herein for modifying modulated signals for transmission. The system receives a modulated signal comprising a speech signal and a carrier wave and generates first and second spectral signals by converting the modulation signal and carrier wave from the time domain to the frequency domain respectively. The system then determines spectral bands for the first and second spectral signals. For each spectral band, the system calculates a weighted spectral band value based on a magnitude of the first spectral signal within the spectral band and generates a modified spectral signal by modifying the second spectral signal with the weighted spectral band value. The system then converts the modified spectral signal from the frequency domain to the time domain and transmits the converted modified spectral signal to a server.

Abstract: Systems and methods for determining the answers to user queries regarding content, and presenting options for the user to view the answers. Searches are conducted to determine the answer to a query and, once the answer is found, various options are presented to the user, allowing the user the choice to view the answer in multiple different ways.

Abstract: The system provides a voice command recommendation to a user to avoid a non-voice command. The system determines a command that is expected to be received, and generates a voice command recommendation that corresponds to the predicted command. The predicted command can be based on the user's behavior, a plurality of users' behavior, environmental circumstances such as a phone call ring, or a combination thereof. The system may access one or more databases to determine the predicted command. The voice command recommendation may include a displayed notification that describes the recommended voice command, and exemplary voice inputs that are recognized. The system also activates an audio interface, such as a microphone, that is configured to receive a voice input. If the system receives a recognizable voice input at the audio interface that corresponds to the recommendation, the system performs the predicted command in response to receiving the voice input.

Abstract: Systems and methods for determining the answers to user queries regarding content, and presenting options for the user to view the answers. Searches are conducted to determine the answer to a query and, once the answer is found, various options are presented to the user, allowing the user the choice to view the answer in multiple different ways.

Abstract: Systems and methods are disclosed herein for modifying modulated signals for transmission. The system receives a modulated signal comprising a speech signal and a carrier wave and generates first and second spectral signals by converting the modulation signal and carrier wave from the time domain to the frequency domain respectively. The system then determines spectral bands for the first and second spectral signals. For each spectral band, the system calculates a weighted spectral band value based on a magnitude of the first spectral signal within the spectral band and generates a modified spectral signal by modifying the second spectral signal with the weighted spectral band value. The system then converts the modified spectral signal from the frequency domain to the time domain and transmits the converted modified spectral signal to a server.