Abstract: Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. Narrative analytics that are linked to communication goal statements can employ a conditional outcome framework that allows the content and structure of resulting narratives to intelligently adapt as a function of the nature of the data under consideration. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal.

Abstract: A digital audio quality monitoring device uses a deep neural network (DNN) to provide accurate estimates of signal-to-noise ratio (SNR) from a limited set of features extracted from incoming audio. Some embodiments improve the SNR estimate accuracy by selecting a DNN model from a plurality of available models based on a codec used to compress/decompress the incoming audio. Each model has been trained on audio compressed/decompressed by a codec associated with the model, and the monitoring device selects the model associated with the codec used to compress/decompress the incoming audio. Other embodiments are also provided.

Abstract: A computer-implemented technique identifies terms in an original reference transcription and original ASR output results that are considered valid variants of each other, even though these terms have different textual forms. Based on this finding, the technique produces a normalized reference transcription and normalized ASR output results in which valid variants are assigned the same textual form. In some implementations, the technique uses the normalized text to develop a model for an ASR system. For example, the technique may generate a word error rate (WER) measure by comparing the normalized reference transcription with the normalized ASR output results, and use the WER measure as guidance in developing the model. Some aspects of the technique involve identifying occasions in which a term can be properly split into component parts. Other aspects can identify other ways in which two terms may vary in spelling, but nonetheless remain valid variants.

Abstract: A system includes a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to receive an audio input representing a percussion performed by a user and classify, at a trained neural network, the audio input as a particular musical type.

Abstract: An audio stream is detected during a communication session with a user. Natural language processing on the audio stream is performed to update a set of attributes by supplementing the set of attributes based on attributes derived from the audio stream. A set of filter values is updated based on the updated set of attributes. The updated set of filter values is used to query a set of databases to obtain datasets. A probabilistic program is executed during the communication session by determining a set of probability parameters characterizing a probability of an anomaly occurring based on the datasets and the set of attributes. A determination is made if whether the probability satisfies a threshold. In response to a determination that the probability satisfies the threshold, a record is updated to identify the communication session to indicate that the threshold is satisfied.

Abstract: Disclosed is an approach to implement new behavior using natural language, and to debug and examine what happened in the past via a natural language interface as well. Some approaches use a combination of natural language understanding techniques, knowledge representation techniques, advanced compiler techniques and user interaction techniques, to solve for natural language programming and debugging of computers.

Abstract: Disclosed herein is a method for providing real-time trustworthiness analysis. The method comprises the steps of: receiving, by a speech data receiving module, speech data; delivering, by the speech data receiving module, the speech data to a speech analysis module; analyzing, by the speech analysis module, the speech data to identify one or more speech attributes; quantifying, by the speech analysis module, at least one of the speech attributes with an attribute score; and determining, by a trustworthiness determination module, a trustworthiness level based on the attribute score of the at least one of the speech attributes.

Abstract: According to some aspects, disclosed methods and systems may include having a user input one or more speech commands into an input device of a user device. The user device may communicate with one or more components or devices at a local office or headend. The local office or the user device may transcribe the speech commands into language transcriptions. The local office or the user device may determine a mood for the user based on whether any of the speech commands may have been repeated. The local office or the user device may determine, based on the mood of the user, which content asset or content service to make available to the user device.

Abstract: Systems and methods are disclosed and described for correcting errors in ASR transcriptions. For an incorrect transcription, different words or phrases from the transcription, and/or related words or phrases, are submitted as hint words to the ASR system, and the voice query is submitted again, to determine new transcriptions. This process is repeated with different transcription terms, until a different and more proper transcription is generated. This increases the accuracy of ASR systems.

Abstract: A conversation augmentation system can automatically augment a conversation with content items based on natural language from the conversation. The conversation augmentation system can select content items to add to the conversation based on determined user “intents” generated using machine learning models. The conversation augmentation system can generate intents for natural language from various sources, such as video chats, audio conversations, textual conversations, virtual reality environments, etc. The conversation augmentation system can identify constraints for mapping the intents to content items or context signals for selecting appropriate content items. In various implementations, the conversation augmentation system can add selected content items to a storyline the conversation describes or can augment a platform in which an unstructured conversation is occurring.

Abstract: Provided are a computer system for transmitting audio content to realize a user-customized being-there and a method thereof. The computer system may be configured to detect audio files that are generated for a plurality of objects at a venue, respectively, and metadata including spatial features that are set for the objects at the venue, respectively, and to transmit the audio files and the metadata for a user. An electronic device of the user may realize a being-there at the venue by rendering the audio files based on the spatial features in the metadata. That is, the user may feel a user-customized being-there as if the user directly listens to audio signals generated from corresponding objects at a venue in which the objects are provided.

Abstract: The present disclosure provides projection neural networks and example applications thereof. In particular, the present disclosure provides a number of different architectures for projection neural networks, including two example architectures which can be referred to as: Self-Governing Neural Networks (SGNNs) and Projection Sequence Networks (ProSeqoNets). Each projection neural network can include one or more projection layers that project an input into a different space. For example, each projection layer can use a set of projection functions to project the input into a bit-space, thereby greatly reducing the dimensionality of the input and enabling computation with lower resource usage. As such, the projection neural networks provided herein are highly useful for on-device inference in resource-constrained devices. For example, the provided SGNN and ProSeqoNet architectures are particularly beneficial for on-device inference such as, for example, solving natural language understanding tasks on-device.

Abstract: A supervised discriminator for detecting bio-markers in an audio sample dataset is trained and a denoising autoencoder is trained to learn a latent space that is used to reconstruct an output audio sample with a same fidelity as an input audio sample of the audio sample dataset. A conditional auxiliary generative adversarial network (GAN) trained to generate the output audio sample with the same fidelity as the input audio sample, wherein the output audio sample is void of the bio-markers. The conditional auxiliary generative adversarial network (GAN), the corresponding supervised discriminator, and the corresponding denoising autoencoder are deployed in an audio processing system.

Abstract: An estimation device (100), which is an estimation device that estimates a duration of a speech section, includes: a representation conversion unit (11) that performs representation conversion of a plurality of words included in learning utterance information to a plurality of pieces of numeric representation data; an estimation data generation unit (12) that generates estimation data by using a plurality of pieces of the learning utterance information and the plurality of pieces of numeric representation data; an estimation model learning unit (13) that learns an estimation model by using the estimation data and the durations of the plurality of words; and an estimation unit (20) that estimates the duration of a predetermined speech section based on utterance information of a user by using the estimation model.

Abstract: Techniques are disclosed for generating anomaly scores for a neuro-linguistic model of input data obtained from one or more sources. According to one embodiment, generating anomaly scores includes receiving a stream of symbols generated from an ordered stream of normalized vectors generated from input data received from one or more sensor devices during a first time period. Upon receiving the stream of symbols, generating a set of words based on an occurrence of groups of symbols from the stream of symbols, determining a number of previous occurrences of a first word of the set of words, determining a number of previous occurrences of words of a same length as the first word, and determining a first anomaly score based on the number of previous occurrences of the first word and the number of previous occurrences of words of the same length as the first word.

Abstract: Embodiments of the present disclosure provide methods and systems for processing a speech signal. The method can include: processing the speech signal to generate a plurality of speech frames; generating a first number of acoustic features based on the plurality of speech frames using a frame shift at a given frequency; and generating a second number of posteriori probability vectors based on the first number of acoustic features using an acoustic model, wherein each of the posteriori probability vectors comprises probabilities of the acoustic features corresponding to a plurality of modeling units, respectively.

Abstract: Implementations set forth herein relate to pre-emptively initializing an automated assistant in a vehicle according to certain indications, in order to reduce latency while also seeking to preserve computational resources. In some implementations, data for effectuating one or more features of an automated assistant can be loaded into memory of a computing device based on vehicle interaction data. For example, the vehicle interaction data can characterize instances in which the user, from within their vehicle, invoked the automated assistant within a threshold period of time of an application completing an operation. Based on the vehicle interaction data, subsequent instances of the operation being completed while the user is in the vehicle can cause data to be loaded into memory in order to pre-emptively prepare the automated assistant to be utilized by the user.

Abstract: An apparatus for processing an audio input recording to obtain a processed audio recording according to an embodiment is provided. The apparatus comprises an input interface (110) for receiving a plurality of audio input portions of the audio input recording. Moreover, the apparatus comprises a processor (120) for processing a plurality of audio input portions of the audio input recording to obtain a processed audio recording. The processor (120) is configured to determine, whether or not an audio input portion of the plurality of audio input portions comprises speech. If the processor (120) has detected that the audio input portion comprises speech, the processor (120) is configured to generate the processed audio recording by modifying the audio input portion to obtain a modified audio portion, and by generating the processed audio recording such that the processed audio recording comprises the modified audio portion instead of the audio input portion.

Abstract: The present invention improves the accuracy of language prediction. A history speech meta-information understanding unit 11 obtains a history speech meta-information vector from a word string of a preceding speech using a meta-information understanding device. A history speech embedding unit 12 converts the word string of the preceding speech and a speaker label into a history speech embedding vector. A speech unit combination vector construction unit 13 obtains a speech unit combination vector by combining the history speech meta-information vector and the history speech embedding vector. A speech sequence embedding vector calculation unit 14 converts a plurality of speech unit combination vectors obtained for the past speech sequences to a speech sequence embedding vector. A language model score calculation unit 15 calculates a language model score of a current speech from a word string of the current speech, a speaker label, and a speech sequence embedding vector.

Abstract: A response generating apparatus (10) includes an acquiring unit (40) that acquires input information that is a trigger for generating a response with respect to a user and context information that is information indicating a situation of the user and a response generating unit (50) that generates, based on the context information acquired from the user, a response associated with the input information.