Abstract: A computer-implemented method includes receiving enrollment audio from a user comprising a wake word to be enrolled for the device, preprocessing the enrollment audio to obtain a vector representation along at least a feature dimension and a temporal dimension, inputting the extracted vector representation to a trained encoding model to generate an embedding representation of the enrollment audio, wherein the encoding model includes a plurality of mixing blocks, and wherein the feature dimension and the temporal dimension of an output of a first layer of each mixing block are flipped for inputting to a second layer of the mixing block, and storing the generated embedding representation in a memory for use in detecting input of the enrolled wake word.

Abstract: There is provided a method of speech-to-speech translation including receiving at a mobile device input speech data associated with speech in a first language and converting the input speech data into input text data using a speech-to-text conversion engine (STT engine) onboard the mobile device. The method also includes translating the input text data to form a translated text data using a text-to-text translation engine (TTT engine) onboard the mobile device. The translated text data is associated with a second language. In addition, the method includes converting the translated text data into output speech data using a text-to-speech conversion engine (TTS engine) onboard the mobile device, and outputting at the mobile device a device output based on the output speech data. Mobile devices and computer-readable storage media for speech-to-speech translation are also provided.

Abstract: There are provided methods and systems for multi-label classification of a sentence. An example method includes obtaining the sentence and generating a first digital representation corresponding to the words of the sentence. The method also includes performing a first classification of the sentence using a classification engine receiving as input the first digital representation. The first classification generates a first set of probabilities each associated with one of the possible labels for the sentence. The classification engine may include a neural network. The method further includes generating an output probability for each given label of the possible labels, which output probability is generated based on a first probability associated with the given label. The first probability is from the first set of probabilities. Moreover, the method includes outputting the output probability for each of the possible labels.

Abstract: There are provided methods and systems for multi-label classification of a sentence. An example method includes obtaining the sentence and generating a first digital representation corresponding to the words of the sentence. Th method also includes performing a first classification of the sentence using a classification engine receiving as input the first digital representation. The first classification generates a first set of probabilities each associated with one of the possible labels for the sentence. The classification engine may include a neural network. The method further includes generating an output probability for each given label of the possible labels, which output probability is generated based on a first probability associated with the given label. The first probability is from the first set of probabilities. Moreover, the method includes outputting the output probability for each of the possible labels.