Abstract: Systems and processes for operating an intelligent automated assistant are provided. For example, a first speech input is received from a user. In response to receiving the first speech input, a response is provided. A first output is provided corresponding to a digital assistant in a first state, and a second speech input is received from the user. A first plurality of values is obtained. Based on the first plurality of values, a first confidence level corresponding to the second speech input is obtained. In accordance with a determination that the first confidence level exceeds a first threshold confidence level, a second output is provided corresponding to the digital assistant in a second state. The second speech input continues to be received.

Abstract: Some embodiments provide a method of training a MT network to detect a wake expression that directs a digital assistant to perform an operation based on a request that follows the expression. The MT network includes processing nodes with configurable parameters. The method iteratively selects different sets of input values with known sets of output values. Each of a first group of input value sets includes a vocative use of the expression. Each of a second group of input value sets includes a non-vocative use of the expression. For each set of input values, the method uses the MT network to process the input set to produce an output value set and computes an error value that expresses an error between the produced output value set and the known output value set. Based on the error values, the method adjusts configurable parameters of the processing nodes of the MT network.

Abstract: Implementations are directed to dynamically adapting which assistant on-device model(s) are locally stored at assistant devices of an assistant device group and/or dynamically adapting the assistant processing role(s) of the assistant device(s) of the assistant device group. In some of those implementations, the corresponding on-device model(s) and/or corresponding processing role(s), for each of the assistant devices of the group, is determined based on collectively considering individual processing capabilities of the assistant devices of the group. Implementations are additionally or alternatively directed to cooperatively utilizing assistant devices of a group, and their associated post-adaptation on-device model(s) and/or post-adaptation processing role(s), in cooperatively processing assistant requests that are directed to any one of the assistant devices of the group.

Abstract: Techniques facilitating interpretation of deep neural model based dialogue agents are provided. In one example, a computer-implemented method comprises extracting, by a device operatively coupled to a processor, features from a dialogue model independently from the dialogue model, the features comprising input features provided to the dialogue model and output features produced via the dialogue model in response to the input features, resulting in extracted features; and analyzing, by the device, a dialogue context associated with the extracted features by identifying pairwise interactions between respective ones of the extracted features.

Abstract: A method and an apparatus for detecting correctness of a pitch period, where the method for detecting correctness of a pitch period includes determining, according to an initial pitch period of an input signal in a time domain, a pitch frequency bin of the input signal, where the initial pitch period is obtained by performing open-loop detection on the input signal, determining, based on an amplitude spectrum of the input signal in a frequency domain, a pitch period correctness decision parameter, associated with the pitch frequency bin, of the input signal, and determining correctness of the initial pitch period according to the pitch period correctness decision parameter.

Abstract: Detection precision of a non-verbal sound is improved. An acoustic model storage unit 10A stores an acoustic model that is configured by a deep neural network with a bottleneck structure, and estimates a phoneme state from a sound feature value. A non-verbal sound model storage unit 10B stores a non-verbal sound model that estimates a posterior probability of a non-verbal sound likeliness from the sound feature value and a bottleneck feature value. A sound feature value extraction unit 11 extracts a sound feature value from an input sound signal. A bottleneck feature value estimation unit 12 inputs the sound feature value to the acoustic model and obtains an output of a bottleneck layer of the acoustic model as a bottleneck feature value. A non-verbal sound detection unit 13 inputs the sound feature value and the bottleneck feature value to the non-verbal sound model and obtains the posterior probability of the non-verbal sound likeliness output by the non-verbal sound model.

Abstract: A first electronic device includes an interface; microphone circuitry; memory circuitry; and processor circuitry. The first electronic device is configured to discover, via the interface, a first wireless network. The first electronic device is configured to receive, via the interface, from a second electronic device discovering the first wireless network, a voice biometric indicative of a second user. The first electronic device is configured to activate the microphone circuitry to detect an input audio signal. The first electronic device is configured to determine whether the detected input audio signal satisfies one or more criteria. The first electronic device is configured to, when the detected input audio signal satisfies the one or more criteria, determine a parameter indicative of a level of risk of exposure; and generate, based on the parameter, contact data.

Abstract: A virtual private assistant (VPA) is configured to analyze various types of input that indicate one or more behaviors associated with a user and to determine the emotional state of the user based on the input. The VPA also determines one or more operations to perform on behalf of the user based on the input and the determined emotional state. The VPA then executes the one or more operations and synthesizes an output based on the emotional state of the user and the one or more operations. The synthesized output includes one or more semantic components and one or more emotional components derived from the emotional state of the user. The VPA observes the behavior of the user in response to the synthesized output and then implements various modifications, based on the observed behavior, to improve the effectiveness of future interactions with the user.

Abstract: A syntactic analysis apparatus according to an embodiment of the present disclosure may include an input device receiving a phrase uttered from a user, and a learning device performing at least one or more of extension of an intent output layer for classifying an utterance intent of the user from the uttered phrase and extension of a slot output layer for classifying a slot including information of the phrase and extending a pre-generated utterance syntactic analysis model, such that the uttered phrase is classified into the extended intent output layer and the extended slot output layer, thereby broadly classifying an intent and a slot for the phrase uttered from a user.

Abstract: Systems, apparatuses, and methods are described for a privacy blocking device configured to prevent receipt, by a listening device, of video and/or audio data until a trigger occurs. A blocker may be configured to prevent receipt of video and/or audio data by one or more microphones and/or one or more cameras of a listening device. The blocker may use the one or more microphones, the one or more cameras, and/or one or more second microphones and/or one or more second cameras to monitor for a trigger. The blocker may process the data. Upon detecting the trigger, the blocker may transmit data to the listening device. For example, the blocker may transmit all or a part of a spoken phrase to the listening device.

Abstract: A speech recognition method and apparatus for performing speech recognition in response to an activation word determined based on a situation are provided. The speech recognition method and apparatus include an artificial intelligence (AI) system and its application, which simulates functions such as recognition and judgment of a human brain using a machine learning algorithm such as deep learning.

Abstract: This disclosure describes, in part, techniques for identifying user-specific content items and/or time-based content items, as well as techniques for providing the content items to the user. For instance, a remote system may receive first audio data from an electronic device, where the first audio data represents a request to identify content related to a topic. The remote system can then store the topic in a database and use the topic to identify the content. Later, the remote system can then receive second audio data from the electronic device, where the second audio data represents a request for the content. The remote system can then send third audio data to the electronic device that represents at least a portion of the content.

Abstract: An electronic apparatus includes: a communication device configured to receive a signal from each of a plurality of acceleration sensors attached to a face of a user; a memory configured to store a classification learning model that classifies words based on a plurality of sensor output values; and a processor configured to determine a word corresponding to a mouth shape of the user by input a value of the received signal to the classification learning model, when the signal is received from each of the plurality of acceleration sensors.

Abstract: A natural language processing system includes logic circuitry that receives, from a first user channel, a user query including natural language (NL) input in a colloquial format from an end user, selects a first NLP application from a plurality of NLP applications, transforms, using the first NLP application, the NL input into a plurality of language tokens, extracts a command trigger from the language tokens, determines that the command trigger is linked to a command executable by a first wagering game channel, transmits a command query incorporating the command to the first wagering game channel, receives a command reply including wagering game data associated with the command from the first wagering game channel, generates, using the first NLP application, an NL response that includes the wagering game data and is structured in a colloquial format, and transmits the NL response to the end user via the first user channel.

Abstract: Various implementations disclosed herein include devices, systems, and methods for indicating whether a conversation regarding a subject satisfies a boundary condition associated with the subject. In various implementations, a device includes a display, a processor and a non-transitory memory. In some implementations, the method includes detecting a conversation in which a person is conveying information regarding a subject. In some implementations, the method includes determining whether the information satisfies a boundary condition associated with the subject. In some implementations, the boundary condition is defined by a set of one or more content items related to the subject. In some implementations, the method includes displaying an indicator that indicates whether or not the information being conveyed by the person satisfies the boundary condition associated with the subject.

Abstract: A computer-implemented method for synthesizing speech audio includes obtaining a grammatical profile defining an input text of actual words as a function of at least syllable-occurrence rates and syllable-count-per-word rates; generating a dictionary of pseudo-words having the syllable-count-per-word rates, each pseudo-word consisting of one syllable or concatenated syllables selected from the input text, wherein substantially all of the pseudo-words are not actual words; constructing an output text product having the grammatical profile, the output text product comprising at least one sentence consisting of one or more pseudo-words selected from the dictionary; and synthesizing speech audio using the output text product. Related systems and computer-readable media are also provided.

Abstract: Background noise estimators and methods are disclosed for estimating background noise in an audio signal. Some methods include obtaining at least one parameter associated with an audio signal segment, such as a frame or part of a frame, based on a first linear prediction gain, calculated as a quotient between a residual signal from a 0th-order linear prediction and a residual signal from a 2nd-order linear prediction for the audio signal segment. A second linear prediction gain is calculated as a quotient between a residual signal from a 2nd-order linear prediction and a residual signal from a 16th-order linear prediction for the audio signal segment. Whether the audio signal segment comprises a pause is determined based at least on the obtained at least one parameter; and a background noise estimate is updated based on the audio signal segment when the audio signal segment comprises a pause.

Abstract: Disclosed are a method, an electronic apparatus for detecting tampering audio and a storage medium. The method includes: acquiring a signal to be detected, and performing a wavelet transform of a first preset order on the signal to be detected so as to obtain a first low-frequency coefficient and a first high-frequency coefficient corresponding to the signal to be detected, the number of which is equal to that of the first preset order; performing an inverse wavelet transform on the first high-frequency coefficient having an order greater than or equal to a second preset order so as to obtain a first high-frequency component signal corresponding to the signal to be detected; calculating a first Mel cepstrum feature of the first high-frequency component signal in units of frame, and concatenating the first Mel cepstrum features of a current frame signal and a preset number of frame signals.

Abstract: Systems, apparatuses, and methods are described to increase a signal-to-noise ratio difference between a main channel and reference channel. The increased signal-to-noise ratio difference is accomplished with an adaptive threshold for a desired voice activity detector (DVAD) and shaping filters. The DVAD includes averaging an output signal of a reference microphone channel to provide an estimated average background noise level. A threshold value is selected from a plurality of threshold values based on the estimated average background noise level. The threshold value is used to detect desired voice activity on a main microphone channel.

Abstract: A text-based speech synthesis method, a computer device, and a non-transitory computer-readable storage medium are provided. The text-based speech synthesis method includes: a target text to be recognized is obtained; each character in the target text is discretely characterized to generate a feature vector corresponding to each character; the feature vector is input into a pre-trained spectrum conversion model, to obtain a Mel-spectrum corresponding to each character in the target text output by the spectrum conversion model; and the Mel-spectrum is converted to speech to obtain speech corresponding to the target text.