Abstract: An agent automation system includes a memory configured to store a natural language understanding (NLU) framework and a processor configured to execute instructions of the NLU framework to cause the agent automation system to perform actions. These actions comprise: generating an annotated utterance tree of an utterance using a combination of rules-based and machine-learning (ML)-based components, wherein a structure of the annotated utterance tree represents a syntactic structure of the utterance, and wherein nodes of the annotated utterance tree include word vectors that represent semantic meanings of words of the utterance; and using the annotated utterance tree as a basis for intent/entity extraction of the utterance.

Abstract: An apparatus for calculating acoustic score, a method of calculating acoustic score, an apparatus for speech recognition, a method of speech recognition, and an electronic device including the same are provided. An apparatus for calculating acoustic score includes a preprocessor configured to sequentially extract audio frames into windows and a score calculator configured to calculate an acoustic score of a window by using a deep neural network (DNN)-based acoustic model.

Abstract: An information processing method and an electronic device are provided. The method includes an electronic device obtaining an input information through a second collection manner when the electronic device is in a speech collection state for obtaining speech information through a first collection manner, and determining a logic boundary position in relation to a first speech information in accordance with the input information, the first speech information is obtained by the electronic device through the first collection manner which is different from the second collection manner. An electronic device corresponding thereto is also disclosed.

Abstract: A computing system receives requests from client devices to process voice queries that have been detected in local environments of the client devices. The system identifies that a value that is based on a number of requests to process voice queries received by the system during a specified time interval satisfies one or more criteria. In response, the system triggers analysis of at least some of the requests received during the specified time interval to trigger analysis of at least some received requests to determine a set of requests that each identify a common voice query. The system can generate an electronic fingerprint that indicates a distinctive model of the common voice query. The fingerprint can then be used to detect an illegitimate voice query identified in a request from a client device at a later time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing sequential data. In one aspect, a computer-implemented method includes receiving a request to generate a system output for an input data sequence, the input data sequence including a plurality of tokens. One or more tokens may be designated as tokens to be skipped. When a token has not been designated as a token to be skipped, the token is processed using a recurrent neural network to update a current internal state of the recurrent neural network. The system output is generated from the final internal state of the recurrent neural network.

Abstract: A speech recognition system includes an input device to receive voice sounds, one or more processors, and one or more storage devices storing parameters and program modules including instructions which cause the one or more processors to perform operations.

Abstract: TTS is a well known technology for decades used for various applications from Artificial Call centers attendants to PC software that allows people with visual impairments or reading disabilities to listen to written works on a home computer. However to date TTS is not widely adopted for PC and Mobile users for daily reading tasks such as reading emails, reading pdf and word documents, reading through website content, and for reading books. The present invention offers new user experience for operating TTS for day to day usage. More specifically this invention describes a synchronization technique for following text being read by TTS engines and specific interfaces for touch pads, touch and multi touch screens. Nevertheless this invention also describes usage of other input methods such as touchpad, mouse, and keyboard.

Abstract: The technology of the present application provides a method and apparatus to manage speech resources. The method includes using a text recognizer to detect a change in a speech application that requires the use of different resources. On detection of the change, the method loads the different resources without the user needing to exit the currently executing speech application.

Abstract: Disclosed herein are systems, methods, and non-transitory computer-readable storage media for speech synthesis. A system practicing the method receives a set of ordered lists of speech units, for each respective speech unit in each ordered list in the set of ordered lists, constructs a sublist of speech units from a next ordered list which are suitable for concatenation, performs a cost analysis of paths through the set of ordered lists of speech units based on the sublist of speech units for each respective speech unit, and synthesizes speech using a lowest cost path of speech units through the set of ordered lists based on the cost analysis. The ordered lists can be ordered based on the respective pitch of each speech unit. In one embodiment, speech units which do not have an assigned pitch can be assigned a pitch.

Abstract: This application discloses a method implemented by an electronic device to detect a signature event (e.g., a baby cry event) associated with an audio feature (e.g., baby sound). The electronic device obtains a classifier model from a remote server. The classifier model is determined according to predetermined capabilities of the electronic device and ambient sound characteristics of the electronic device, and distinguishes the audio feature from a plurality of alternative features and ambient noises. When the electronic device obtains audio data, it splits the audio data to a plurality of sound components each associated with a respective frequency or frequency band and including a series of time windows. The electronic device further extracts a feature vector from the sound components, classifies the extracted feature vector to obtain a probability value according to the classifier model, and detects the signature event based on the probability value.

Abstract: Speech recognition is performed by receiving a speech signal that includes spoken phones. A dynamic time warping procedure is applied to the received speech signal to generate a time-warped signal. The time-warped signal is compared to a plurality of stored reference patterns to identify a set of stored reference patterns that are most similar to the time-warped signal. A candidate hot word is selected from a list using the identified set of stored reference patterns. The selection of the candidate hot word is then refined.

Abstract: The present invention relates to transposing signals in time and/or frequency and in particular to coding of audio signals. More particular, the present invention relates to high frequency reconstruction (HFR) methods including a frequency domain harmonic transposer. A method and system for generating a transposed output signal from an input signal using a transposition factor T is described. The system comprises an analysis window of length La, extracting a frame of the input signal, and an analysis transformation unit of order M transforming the samples into M complex coefficients. M is a function of the transposition factor T. The system further comprises a nonlinear processing unit altering the phase of the complex coefficients by using the transposition factor T, a synthesis transformation unit of order M transforming the altered coefficients into M altered samples, and a synthesis window of length Ls, generating a frame of the output signal.

Abstract: An example operation may include one or more of receiving a plurality of local audio files from a plurality of audio devices that participated in a teleconference, where each local audio file includes a locally captured audio recording of a user of a respective audio device during the teleconference, generating combined audio playback information for the teleconference based on the plurality of local audio files received from the plurality of audio devices, the generating including detecting audio portions within the plurality of local audio files and synchronizing a playing order of the detected audio portions based on timing information included in the plurality of local audio files, and transmitting the combined audio playback information of the teleconference to at least one audio device among the plurality of audio devices.

Abstract: A method and system automates training named entity recognition in natural language processing to build configurable entity definitions includes receiving input documents or entities through an administration module and defining a domain for each entity. Further, one or more entities corresponding to the domain specific entity in the received documents are determined and a training file to one of pick a right parser, extract content and label the entity ambiguity is generated. One or more user actions are collected and maintained at a repository through a knowledge engine. Still further, one or more labelled ambiguous words are predicted and the knowledge engine is updated. Data may be fetched, through a training pipeline execution engine and each entity may be associated with one or more documents based on the fetched data from the document store to build configurable entity definitions.

Abstract: Disclosed are some examples of systems, apparatus, methods and computer program products implementing techniques for extending the range of a set of decoded parameter values for a sequence of frequency bands in an identifiable time frame of an audio signal. In some implementations, the parameter values vary in relation to a sequence of time frames of the audio signal and in relation to a sequence of frequency bands in each time frame. In some implementations, it is determined that a decoded value corresponds to a minimum of a first range of values of a first coding protocol of a set of coding protocols. The determined value is modified to be below the minimum of the first range of values to produce an extended value. A modified set of decoded values including one or more extended values can thus be provided.

Abstract: Methods, apparatus, systems, and computer-readable media for engaging an automated assistant to perform multiple tasks through a multitask command. The multitask command can be a command that, when provided by a user, causes the automated assistant to invoke multiple different agent modules for performing tasks to complete the multitask command. During execution of the multitask command, a user can provide input that can be used by one or more agent modules to perform their respective tasks. Furthermore, feedback from one or more agent modules can be used by the automated assistant to dynamically alter tasks in order to more effectively use resources available during completion of the multitask command.

Abstract: A method, apparatus, and system are provided for resolving conflicts between training data conflicts by retrieving independent training data sets, each comprising a plurality of intents and end-user utterances for use in training one or more classifiers to recognize a corresponding intent from one or more of the end-user utterances, providing a first test end-user utterance associated with a first intent from the first independent training data set to the one or more classifiers to select an output intent generated by the one or more classifiers; identifying a first conflict when the first intent does not match the output intent, and automatically generating, by the system, one or more conflict resolution recommendations for display and selection by an end user to resolve the first conflict.

Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that use an intelligent analytics interface to process natural-language and other inputs to configure an analytics task for the system. The disclosed methods, non-transitory computer readable media, and systems provide the intelligent analytics interface to facilitate an exchange between the systems and a user to determine values for the analytics task. The methods, non-transitory computer readable media, and systems then use these values to execute an analytics task.

Abstract: Systems and methods are disclosed for customizing a neural network for a custom dataset, when the neural network has been trained on data from a general dataset. The neural network may comprise an output layer including one or more nodes corresponding to candidate outputs. The values of the nodes in the output layer may correspond to a probability that the candidate output is the correct output for an input. The values of the nodes in the output layer may be adjusted for higher performance when the neural network is used to process data from a custom dataset.

Abstract: A training method and apparatus for speech recognition is disclosed, where an example of the training method includes determining whether a current iteration for training a neural network is performed by an experience replay iteration using an experience replay set, selecting a sample from at least one of the experience replay set and a training set based on a result of the determining, and training the neural network based on the selected sample.