Abstract: In one embodiment, a method includes receiving a user's utterance comprising a word in a custom vocabulary list of the user, generating a previous token to represent a previous audio portion of the utterance, and generating a current token to represent a current audio portion of the utterance by generating a bias embedding by using the previous token to query a trie of wordpieces representing the custom vocabulary list, generating first probabilities of respective first candidate tokens likely uttered in the current audio portion based on the bias embedding and the current audio portion, generating second probabilities of respective second candidate tokens likely uttered after the previous token based on the previous token and the bias embedding, and generating the current token to represent the current audio portion of the utterance based on the first probabilities of the first candidate tokens and the second probabilities of the second candidate tokens.

Abstract: A method of treating a disease or disorder associated with excessive uric acid levels is disclosed herein. The method comprises administering to the subject by i.v. infusion an amount of a recombinant homotetrameric uricase enzyme comprising four uricase polypeptides having the amino acid sequence as set forth in SEQ ID NO: 2, wherein the polypeptides are crosslinked by polyethylene glycol (PEG) bis-aldehyde having a molecular weight of 2-3.5 kDa.

Abstract: The disclosure relates to an audio processing apparatus, comprising: a plurality of audio sensors, each audio sensor configured to receive a respective plurality of audio frames of an audio signal from an audio source, wherein the respective plurality of audio frames defines an audio channel of the audio signal; and a processing circuitry configured to: determine a respective feature set having at least one feature for each audio frame of each of the plurality of audio frames, wherein the plurality of features define a three-dimensional feature array; process the three-dimensional feature array using a neural network, wherein the neural network comprises a self-attention layer configured to process a plurality of two-dimensional sub-arrays of the three-dimensional feature array; and generate an output signal on the basis of the plurality of processed two-dimensional sub-arrays. Moreover, the disclosure relates to a corresponding audio processing method.

Abstract: In one embodiment, a system includes an automatic speech recognition (ASR) module, a natural-language understanding (NLU) module, a dialog manager, one or more agents, an arbitrator, a delivery system, one or more processors, and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to receive a user input, process the user input using the ASR module, the NLU module, the dialog manager, one or more of the agents, the arbitrator, and the delivery system, and provide a response to the user input.

Abstract: The disclosure relates to an audio processing apparatus, comprising: a plurality of audio sensors, each audio sensor configured to receive a respective plurality of audio frames of an audio signal from an audio source, wherein the respective plurality of audio frames defines an audio channel of the audio signal; and a processing circuitry configured to: determine a respective feature set having at least one feature for each audio frame of each of the plurality of audio frames, wherein the plurality of features define a three-dimensional feature array; process the three-dimensional feature array using a neural network, wherein the neural network comprises a self-attention layer configured to process a plurality of two-dimensional sub-arrays of the three-dimensional feature array; and generate an output signal on the basis of the plurality of processed two-dimensional sub-arrays. Moreover, the disclosure relates to a corresponding audio processing method.