Abstract: A solar tracker having at least one post (7) for fixing to the ground at least one solar panel (8) that rotates by action of a drive, which has a rotation transmission support (1) on which the solar panel (8) is supported and which is coupled to the post (7) in such a way that it slides along a horizontal guide rail (2) fixed to the post (7) by a guide element (3), and the rotation transmission support (1) rolling on a rolling contact element (4) fixed to the post (7) to establish the rotation of the solar panel (8) in both directions.

Abstract: A weak base anion resin has alkylamine segments covalently bound to cross-linking segments, the alkylamine segments being selected from a group consisting of: vinylamine, N-methylvinylamine, N,N-dimethylvinylamine, N-methyl-N-ethylvinylamine, and N,N-diethylvinylamine, and the weak base anion resin including ions adsorbed to the alkylamine segments. A method for ion exchange includes providing a weak base anion resin, contacting the weak base anion resin with a solution containing ions to one of capture or adsorb ions from solution to the weak base anion resin, and regenerating the weak base anion resin. A method to capture and release an acid gas includes providing a weak base anion resin, contacting the weak base anion resin with a gas including an acid gas to one of capture or adsorb the acid gas from the gas to the weak base anion resin, and regenerating the weak base anion resin to release the acid gas.

Abstract: An optical system configured to apply a relative phase modulation between two orthogonal polarization modes using a double pass configuration, wherein the system is configured to act independently on each polarization state.

Abstract: Disclosed is a method for producing products from warm dough, which includes producing a dough by means of cooking; dispensing the dough into a first container up to a predetermined level; inserting, while the dough is warm, a first forming cutting mold provided with a plurality of tubular cells in the shape of the product to be obtained, for example, a croquette, to fill each of the cells with the dough; and rapidly cooling, thereby forming the croquettes, which are pushed out by means of a first ejector mold to obtain a plurality of croquettes simultaneously. Any leftover dough is dispensed warm into a second container provided with a movable separator, which moves after dispensing, until the dough reaches the predetermined level, and the previous steps are repeated.

Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

Abstract: The technology described in this document can be embodied in a computer-implemented method that includes receiving, at a processing system, a first signal including an output of a speaker device and an additional audio signal. The method also includes determining, by the processing system, based at least in part on a model trained to identify the output of the speaker device, that the additional audio signal corresponds to an utterance of a user. The method further includes initiating a reduction in an audio output level of the speaker device based on determining that the additional audio signal corresponds to the utterance of the user.

Abstract: A speaker verification method includes receiving audio data corresponding to an utterance, processing a first portion of the audio data that characterizes a predetermined hotword to generate a text-dependent evaluation vector, and generating one or more text-dependent confidence scores. When one of the text-dependent confidence scores satisfies a threshold, the operations include identifying a speaker of the utterance as a respective enrolled user associated with the text-dependent confidence score that satisfies the threshold and initiating performance of an action without performing speaker verification. When none of the text-dependent confidence scores satisfy the threshold, the operations include processing a second portion of the audio data that characterizes a query to generate a text-independent evaluation vector, generating one or more text-independent confidence scores, and determining whether the identity of the speaker of the utterance includes any of the enrolled users.

Abstract: Techniques are disclosed that enable processing of audio data to generate one or more refined versions of audio data, where each of the refined versions of audio data isolate one or more utterances of a single respective human speaker. Various implementations generate a refined version of audio data that isolates utterance(s) of a single human speaker by processing a spectrogram representation of the audio data (generated by processing the audio data with a frequency transformation) using a mask generated by processing the spectrogram of the audio data and a speaker embedding for the single human speaker using a trained voice filter model. Output generated over the trained voice filter model is processed using an inverse of the frequency transformation to generate the refined audio data.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: A network node configured to operate in an optical fiber network can comprise a quantum key distribution (QKD) communication unit adapted to communicate with another QKD communication unit of at least one other network node of the optical fiber network according to a CV-QKD mode and/or a DV-QKD mode. A control unit can be configured to control the QKD communication unit to operate in at least one of the CV-QKD mode and the DV-QKD mode. The control unit can be configured to switch operation of the QKD communication unit between the CV-QKD mode and the DV-QKD mode.

Abstract: This document generally describes systems, methods, devices, and other techniques related to speaker verification, including (i) training a neural network for a speaker verification model, (ii) enrolling users at a client device, and (iii) verifying identities of users based on characteristics of the users' voices. Some implementations include a computer-implemented method. The method can include receiving, at a computing device, data that characterizes an utterance of a user of the computing device. A speaker representation can be generated, at the computing device, for the utterance using a neural network on the computing device. The neural network can be trained based on a plurality of training samples that each: (i) include data that characterizes a first utterance and data that characterizes one or more second utterances, and (ii) are labeled as a matching speakers sample or a non-matching speakers sample.

Abstract: A speaker verification method includes receiving audio data corresponding to an utterance, processing the audio data to generate a reference attentive d-vector representing voice characteristics of the utterance, the evaluation ad-vector includes ne style classes each including a respective value vector concatenated with a corresponding routing vector. The method also includes generating using a self-attention mechanism, at least one multi-condition attention score that indicates a likelihood that the evaluation ad-vector matches a respective reference ad-vector associated with a respective user. The method also includes identifying the speaker of the utterance as the respective user associated with the respective reference ad-vector based on the multi-condition attention score.

Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.

Abstract: A bearing for fastening rotating shafts on support posts is provided, having a support (1) by which a clamp (3) is fastened on the support post (2), incorporating in the clamp (3) a ball joint (4.1) through which the rotating shaft (5) to be fastened passes, wherein the support (1) is made up of two complementary portions, which are axially attached facing one another, retaining the support post (2) between them, whereas on the assembly of the mentioned support (1) there is fixed a clamp (3) which houses internally a spherical ball joint (4.1) which determines a central opening through which the rotating shaft (5) to be fastened passes.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

Abstract: Speaker diarization techniques that enable processing of audio data to generate one or more refined versions of the audio data, where each of the refined versions of the audio data isolates one or more utterances of a single respective human speaker. Various implementations generate a refined version of audio data that isolates utterance(s) of a single human speaker by generating a speaker embedding for the single human speaker, and processing the audio data using a trained generative model—and using the speaker embedding in determining activations for hidden layers of the trained generative model during the processing. Output is generated over the trained generative model based on the processing, and the output is the refined version of the audio data.

Abstract: A bearing (1) for rotational shafts supported by support pillars, to establish the fastening between a rotational shaft (3) and support pillars (2), able to rotate freely, having a support (4) which is fixed to the support pillar (2), the support (4) being axially clamped between two component parts (6.1), (6.2) of a clamp (6), which determines a ring (7) that houses a ball joint (5), through which the rotational shaft (3) being fastened is arranged.

Abstract: Determining a language for speech recognition of a spoken utterance received via an automated assistant interface for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Implementations determine a user profile that corresponds to audio data that captures a spoken utterance, and utilize language(s), and optionally corresponding probabilities, assigned to the user profile in determining a language for speech recognition of the spoken utterance. Some implementations select only a subset of languages, assigned to the user profile, to utilize in speech recognition of a given spoken utterance of the user.

Abstract: Implementations relate to determining a language for speech recognition of a spoken utterance, received via an automated assistant interface, for interacting with an automated assistant. Implementations can enable multilingual interaction with the automated assistant, without necessitating a user explicitly designate a language to be utilized for each interaction. Selection of a speech recognition model for a particular language can based on one or more interaction characteristics exhibited during a dialog session between a user and an automated assistant. Such interaction characteristics can include anticipated user input types, anticipated user input durations, a duration for monitoring for a user response, and/or an actual duration of a provided user response.