Abstract: Systems and methods are provided for smart audio segmentation using look-ahead based acousto-linguistic features. For example, systems and methods are provided for obtaining audio, processing the audio, identifying a potential segmentation boundary within the audio, and determining whether to generate a segment break at the potential segmentation boundary. One or more look-ahead words occurring after the potential segmentation boundary are identified, wherein an acoustic segmentation score and a language segmentation score associated with the potential segmentation boundary and the one or more look-ahead words are generated. Systems then either refrain from generating a segment break at the potential segmentation boundary or generate the segment break at the potential segmentation boundary based on the acoustic and/or language segmentation score at least meeting or exceeding a segmentation score threshold.

Abstract: Some disclosed embodiments are directed to obtaining a decoded audio data including a spoken language utterance recognized in audio data and identifying a disfluency in the decoded audio data. Upon determining that correcting the disfluency would improve a readability score of the decoded audio data, the system generates a particular correction to correct the disfluency and applies the particular correction to the decoded audio data. Then, an updated decoded audio data is generated which reflects the particular correction. The updated decoded audio data has improved readability over the decoded audio data.

Abstract: Systems are configured to obtain streaming audio data comprising language utterances, continuously decode the streaming audio data in order to generate decoded streaming audio data and determine whether a linguistic boundary exists within an initial segment of decoded streaming audio data. When a linguistic boundary is determined to exist, the systems apply a punctuation at the linguistic boundary and output a first portion of the initial segment of the streaming audio data ending at the linguistic boundary while refraining from outputting a second portion of the initial segment which is located temporally subsequent to the first portion of the initial segment. Systems are also configured to delay the output until predetermined punctuation validation processes have been performed.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: Streaming machine learning unidirectional models is facilitated by the use of embedding vectors. Processing blocks in the models apply embedding vectors as input. The embedding vectors utilize context of future data (e.g., data that is temporally offset into the future within a data stream) to improve the accuracy of the outputs generated by the processing blocks. The embedding vectors cause a temporal shift between the outputs of the processing blocks and the inputs to which the outputs correspond. This temporal shift enables the processing blocks to apply the embedding vector inputs from processing blocks that are associated with future data.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: Streaming machine learning unidirectional models is facilitated by the use of embedding vectors. Processing blocks in the models apply embedding vectors as input. The embedding vectors utilize context of future data (e.g., data that is temporally offset into the future within a data stream) to improve the accuracy of the outputs generated by the processing blocks. The embedding vectors cause a temporal shift between the outputs of the processing blocks and the inputs to which the outputs correspond. This temporal shift enables the processing blocks to apply the embedding vector inputs from processing blocks that are associated with future data.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: A method for facilitating a remote conference includes receiving a digital video and a computer-readable audio signal. A face recognition machine is operated to recognize a face of a first conference participant in the digital video, and a speech recognition machine is operated to translate the computer-readable audio signal into a first text. An attribution machine attributes the text to the first conference participant. A second computer-readable audio signal is processed similarly, to obtain a second text attributed to a second conference participant. A transcription machine automatically creates a transcript including the first text attributed to the first conference participant and the second text attributed to the second conference participant.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: A device may establish a communication session, with a client device, for monitoring a latency of a service. The device may receive, from the client device, a request for a monitored service list. The monitored service list may identify one or more services for which service latency monitoring is supported. The device may provide, to the client device, the monitored service list. The device may receive, from the client device, a service latency monitoring session request that may identify the service to be monitored. The device may establish, with the client device, the service latency monitoring session based on the service latency monitoring session request. The device may cause the service to be performed. The device may generate information for determining the latency of the service. The device may transmit, to the client device and via the service latency monitoring session, the information for determining the latency of the service.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: Techniques are described for extending a two-way active measurement protocol (TWAMP) to enable measurement of service key performance indicators (KPIs) in a software defined network (SDN) and network function virtualization (NFV) architecture. The TWAMP extensions enable control messaging to be handled by a TWAMP control client executed on a centralized controller, and data messaging to be handled by a TWAMP session initiator executed on a separate network device. Techniques are also described for extending TWAMP to enable measurement of any of a plurality of service KPIs for a given service supported at a TWAMP server. The service KPIs may include one or more of keepalive measurements, round trip time measurements, path delay measurements, service latency measurements, or service load measurements. The TWAMP extensions for the service KPIs may be used in both conventional network architectures and in SDN and NFV architectures.

Abstract: Embodiments are directed to optimizing data transfers between heterogeneous memory arenas. In one scenario, a computer system receives an indication that a data chunk is to be transferred from a first memory arena to a third memory arena, and then determines that for the data chunk to be transferred from the first memory arena to the third arena, the data chunk is to be transferred from the first memory arena to a second memory arena, and from the second memory arena to the third memory arena. The computer system divides the data chunk into smaller data portions and copies a first data portion from the first memory arena to the second memory arena. The computer system then copies the first data portion from the second memory arena to the third memory arena and copies a second data portion from the first memory arena to the second memory arena in parallel.

Abstract: A device may establish a communication session, with a client device, for monitoring a latency of a service. The device may receive, from the client device, a request for a monitored service list. The monitored service list may identify one or more services for which service latency monitoring is supported. The device may provide, to the client device, the monitored service list. The device may receive, from the client device, a service latency monitoring session request that may identify the service to be monitored. The device may establish, with the client device, the service latency monitoring session based on the service latency monitoring session request. The device may cause the service to be performed. The device may generate information for determining the latency of the service. The device may transmit, to the client device and via the service latency monitoring session, the information for determining the latency of the service.