A communication server detects a first communication request from a first caller device to a second caller device. The communication server detects a second communication request from the second caller device to the first caller device. The first communication request and the second communication request both occur within a threshold time period. The communication server establishes, without additional input from the first caller device and the second caller device, an active session in response to the first communication request. The active session is established between the first caller device and the second caller device. The communication server dismisses the second communication request.

A new audio quality assessment system includes an assessment system running in a receiver system of a VoIP communication system. The new audio quality assessment system determines an accurate MOS of a VoIP call within a time window. The audio quality assessment system determines an effective PLC counter, a PLC impact factor, an effective AS counter, an AS impact factor, a network impact factor, a codec type of the received voice packets, a bitrate of the received voice packets, an initial MOS from a configured codec-bitrate MOS table, and determines the accurate MOS based on these data. The determined MOS is more accurate and efficiently obtained since it is based on efficiently collected statistics of the receiver system's modules and a pre-configured codec-bitrate MOS table.

An example computing device creates a resource module that acquires resource information relating to resources of the computing device and acquire quality of experience information corresponding to a quality of audio, and creates a roster module that displays a participant list including resource information relating to resources of a plurality of other computing devices connected to the teleconference and display quality of experience information corresponding to a quality of audio a user of each of the plurality of other computing devices experiences when connected to the teleconference. The device also creates a policy module configured to evaluate the resource information and quality of experience information acquired by the client resource module against rules of a teleconference policy and implement actions based on a determination that the computing device is non-compliant with one or more of the rules of the teleconference policy to control quality of audio during the teleconference.

Techniques are provided for evaluating multiple machine learning models to identify issues with a communication. One method comprises applying an audio signal associated with a communication to at least two of: (i) a trigger word analysis module that evaluates contextual information to determine if a trigger word is detected in the audio signal; (ii) an audio activity pattern analysis module that determines if a silence pattern anomaly is detected; and (iii) a communication application analysis module that evaluates features provided by a communication application relative to applicable thresholds; and combining results of the at least two of the trigger word analysis module, the audio activity pattern analysis module and the communication application analysis module to identify a communication issue. The combining may evaluate an accuracy of the trigger word analysis module, the audio activity pattern analysis module and/or the communication application analysis module to combine the results.

The invention relates to a communication system and a method of maintaining audio communication in a congested communication channel currently bearing the transmission of speech in audio communication between a sender side and a receiver side, the communication channel having at least one signaling channel and at least one payload channel having a quality of service. During the audio communication the quality of service of the payload channel is monitored. If the quality of service of the payload channel is below a threshold the speech at the respective sender side is converted to text; and transmitted over the retained communication channel to the respective receiver side. The text may be converted back to speech at the receiver side.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for evaluating the quality of a communication session or of a communication path used for the communication session. One of the methods includes initiating a communication session between a first communications device and a second communications device, wherein initiating the communication session comprises routing session data for the communication session along a first communication path between the first communications device and the second communications device; generating, at the first communications device, a plurality of reference content samples; generating a recording of the communication session as received at a first destination along the first communication path; and evaluating a quality of the communication session or of the first communication path by comparing the plurality of reference content samples with the recorded communication session.

Audio processing is provided to determine whether an audio issue is present within a multi-participant communication system such as a teleconference or videoconference bridge or a trunk dispatch system. Audio issues such as background noise, background conversations, or other unwanted audio that is being interjected into the multi-participant conversation and that may be dominating the audio are detected by measuring characteristics of audio samples taken from the communication ports of the multi-participant communication system. A correction may then be applied to the audio received through the communication port by a processor of the multi-participant communication system without intervention by an administrator, such as by muting the port, applying a noise cancellation to audio from the port, or time-shifting the audio from the port.

A trial number and a failure number of a procedure extending to the plurality of devices configuring the mobile core network are acquired, a process trial number and a failure number of a received message in the single device are acquired, and the possibility of quality degradation of the devices configuring the mobile core network is calculated on the basis of the trial number and the failure numbers.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing a call drop likelihood prediction for an interactive call data object. In accordance with one embodiment, a method is provided that includes: identifying a group of interactive call feature data objects associated with the interactive call data object that comprises an interactive call audio data object and an interactive call metadata object; processing the call feature data objects using a real-time call monitoring machine learning framework to generate the prediction by: processing the call audio data object using an audio data processing machine learning model to generate an audio-based embedding data object, processing the call audio data object using an audio transcript processing machine learning model to generate a transcript-based embedding data object, and generating the prediction based at least in part on the audio-based and transcript-based embedding data objects and the metadata object.

A system, computer program product, and method are disclosed. In an approach to train customer service agent using chatbots. The method includes training a chatbot for a customer chat simulation based on a customer service conversation data, a task scenario, and a customer persona. The method also includes monitoring an interaction between a customer service agent and the chatbot. The method further includes determining an assessment of the performance of the customer service agent based on the interaction between the customer service agent and the chatbot. The method additionally includes generating feedback for the customer service agent based on the assessment of the performance of the customer service agent.