Abstract: Techniques for creating a persistent connection between client devices and one or more remote computing resources, which may form a portion of a network-accessible computing platform. This connection may be considered “permanent” or “nearly permanent” to allow the client device to both send data to and receive data from the remote resources at nearly any time. In addition, both the client device and the remote resources may establish virtual channels over this single connection. If no data is exchanged between the client device and the remote computing resources for a threshold amount of time, then the connection may be severed and the client device may attempt to establish a new connection with the remote computing resources.

Abstract: An open framework for computing devices to dispatch voice-based interactions to supporting applications. Applications are selected on a trial-and-error basis to find an application able to handle the voice interaction. Dispatching to the applications may be performed without a determination of meaning conveyed in the interaction, with meaning determined by the individual applications. Once an application acts upon a voice interaction, that application may be given first-right-of-refusal for subsequent voice interactions.

Abstract: Techniques for creating a persistent connection between client devices and one or more remote computing resources, which may form a portion of a network-accessible computing platform. This connection may be considered “permanent” or “nearly permanent” to allow the client device to both send data to and receive data from the remote resources at nearly any time. In addition, both the client device and the remote resources may establish virtual channels over this single connection. If no data is exchanged between the client device and the remote computing resources for a threshold amount of time, then the connection may be severed and the client device may attempt to establish a new connection with the remote computing resources.

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Abstract: An architecture for selecting and providing content items to a device includes cloud-based resources of a device management service and one or more content sources. The device is configured to receive instructions and a portion of the content item from the device management service. The instructions direct the device to obtain and buffer the content item from the content source, while simultaneously outputting the portion of the content item to a user.

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Abstract: A speech recognition platform configured to receive an audio signal that includes speech from a user and perform automatic speech recognition (ASR) on the audio signal to identify ASR results. The platform may identify: (i) a domain of a voice command within the speech based on the ASR results and based on context information associated with the speech or the user, and (ii) an intent of the voice command. In response to identifying the intent, the platform may perform a corresponding action, such as streaming audio to the device, setting a reminder for the user, purchasing an item on behalf of the user, making a reservation for the user or launching an application for the user. The speech recognition platform, in combination with the device, may therefore facilitate efficient interactions between the user and a voice-controlled device.

Abstract: A speech recognition platform configured to receive an audio signal that includes speech from a user and perform automatic speech recognition (ASR) on the audio signal to identify ASR results. The platform may identify: (i) a domain of a voice command within the speech based on the ASR results and based on context information associated with the speech or the user, and (ii) an intent of the voice command. In response to identifying the intent, the platform may perform a corresponding action, such as streaming audio to the device, setting a reminder for the user, purchasing an item on behalf of the user, making a reservation for the user or launching an application for the user. The speech recognition platform, in combination with the device, may therefore facilitate efficient interactions between the user and a voice-controlled device.

Abstract: A computing system for selecting and providing content items to a device. The device is configured to output a first content item to a user and to detect events related to the output of the first content item and, in response, to provide a notification to a cloud service related to the event. The device is further configured to receive at least a second content item from the cloud services and to buffer the second content item while outputting the first content item.

Abstract: Features are disclosed for generating markers for elements or other portions of an audio presentation so that a speech processing system may determine which portion of the audio presentation a user utterance refers to. For example, an utterance may include a pronoun with no explicit antecedent. The marker may be used to associate the utterance with the corresponding content portion for processing. The markers can be provided to a client device with a text-to-speech (“TTS”) presentation. The markers may then be provided to a speech processing system along with a user utterance captured by the client device. The speech processing system, which may include automatic speech recognition (“ASR”) modules and/or natural language understanding (“NLU”) modules, can generate hints based on the marker. The hints can be provided to the ASR and/or NLU modules in order to aid in processing the meaning or intent of a user utterance.

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Abstract: Features are disclosed for processing a user utterance with respect to multiple subject matters or domains, and for selecting a likely result from a particular domain with which to respond to the utterance or otherwise take action. A user utterance may be transcribed by an automatic speech recognition (“ASR”) module, and the results may be provided to a multi-domain natural language understanding (“NLU”) engine. The multi-domain NLU engine may process the transcription(s) in multiple individual domains rather than in a single domain. In some cases, the transcription(s) may be processed in multiple individual domains in parallel or substantially simultaneously. In addition, hints may be generated based on previous user interactions and other data. The ASR module, multi-domain NLU engine, and other components of a spoken language processing system may use the hints to more efficiently process input or more accurately generate output.

Abstract: Features are disclosed for generating markers for elements or other portions of an audio presentation so that a speech processing system may determine which portion of the audio presentation a user utterance refers to. For example, an utterance may include a pronoun with no explicit antecedent. The marker may be used to associate the utterance with the corresponding content portion for processing. The markers can be provided to a client device with a text-to-speech (“TTS”) presentation. The markers may then be provided to a speech processing system along with a user utterance captured by the client device. The speech processing system, which may include automatic speech recognition (“ASR”) modules and/or natural language understanding (“NLU”) modules, can generate hints based on the marker. The hints can be provided to the ASR and/or NLU modules in order to aid in processing the meaning or intent of a user utterance.

Abstract: A speech-based system includes a local device in a user premises and a remote service that uses the local device to conduct speech dialogs with a user. The local device may also be directed to play audio such as music, audio books, etc. When designating audio for playing by the local device, the remote service may specify that the audio is either background audio or foreground audio. For background audio, the service indicates whether the background audio is mixable. For foreground audio, the service indicates an interrupt behavior. When the local device is playing background audio and receives foreground audio, the background audio is paused, attenuated, or not changed based on the indicated interrupt behavior of the foreground audio and whether the background audio has been designated as being mixable.

Abstract: An architecture for selecting and providing content items to a device includes cloud-based resources of a device management service and one or more content sources. The device is configured to receive instructions and a portion of the content item from the device management service. The instructions direct the device to obtain and buffer the content item from the content source, while simultaneously outputting the portion of the content item to a user.

Abstract: Features are disclosed for generating markers for elements or other portions of an audio presentation so that a speech processing system may determine which portion of the audio presentation a user utterance refers to. For example, an utterance may include a pronoun with no explicit antecedent. The marker may be used to associate the utterance with the corresponding content portion for processing. The markers can be provided to a client device with a text-to-speech (“TTS”) presentation. The markers may then be provided to a speech processing system along with a user utterance captured by the client device. The speech processing system, which may include automatic speech recognition (“ASR”) modules and/or natural language understanding (“NLU”) modules, can generate hints based on the marker. The hints can be provided to the ASR and/or NLU modules in order to aid in processing the meaning or intent of a user utterance.