Abstract: A home interface comprises one or more audio interfaces within the premises of users. The audio interfaces are connected to an assistant service that detects conditions within rooms of user premises and that generates simulated audio themes for the rooms. The audio themes may be changed depending on detected conditions, and subthemes of a given audio theme may be implemented in different rooms.

Abstract: An unsupervised acoustic modeling service for speech recognition is disclosed. A computing device may be present in a listening zone, such as a household or office, and may receive an audio signal that may include speech. In some instances, the speech is not directed to the computing device. Speech recognition results may be generated from the audio signal, using an acoustic model, and the results used to update the acoustic model. For example, the acoustic model may be updated to reflect a particular speaker's pronunciation of certain sound units, such as phonemes. By using speech that is not necessarily directed to the computing device, more data may be available for updating an acoustic model.

Abstract: A language model for speech recognition may be discriminatively pruned. In some embodiments, the language model is discriminatively pruned by computing a discriminative objective function value for one or more n-grams in the language model, and selecting one or more n-grams to prune based at least in part on a threshold value. In some embodiments, the language model is discriminatively pruned to a sufficiently small number of n-grams such that transcription of audio inputs may occur in real time, or such that the pruned language model may be stored on a device with relatively limited electronic storage capacity.

Abstract: Natural language controlled devices may be configured to activate command recognition in response to one or more wake words. Techniques are provided to receive a candidate word for evaluation as a wake word that activates a natural language control functionality of a computing device. The candidate word may include one or more words or sounds. Values for multiple wake word metrics are then determined. The candidate word is evaluated based on the various wake word metrics.

Abstract: Features are disclosed for discriminative training of speech recognition language models. A confusion matrix can be generated from acoustic model training data for use in discriminative training. The confusion matrix can include probabilities for the substitution, insertion, and/or deletion of some or all subword units of a language. Probabilities can be calculated based on the presence or absence of subword units in a processed acoustic model training data audio recording when compared to a correct transcription of the recording. The probabilities can be used to generate erroneous transcriptions in language model training corpora, and the language model can be trained to distinguish the erroneous transcriptions from the correct transcriptions.