Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for speech recognition using attention-based sequence-to-sequence models. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A sequence of feature vectors indicative of the acoustic characteristics of the utterance is generated. The sequence of feature vectors is processed using a speech recognition model that has been trained using a loss function that uses N-best lists of decoded hypotheses, the speech recognition model including an encoder, an attention module, and a decoder. The encoder and decoder each include one or more recurrent neural network layers. A sequence of output vectors representing distributions over a predetermined set of linguistic units is obtained. A transcription for the utterance is obtained based on the sequence of output vectors. Data indicating the transcription of the utterance is provided.

Abstract: Aspects of the subject disclosure may include, for example, a device that includes a processing system having a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, where the operations include determining parameters for adapting audio in the content to the device, wherein the device renders the content, and wherein the parameters are based on semantic metadata embedded in the content, adapting the audio in the content based on the parameters, and rendering the content, as adapted by the parameters, to represent a semantic in the semantic metadata. Other embodiments are disclosed.