Systems and methods for communications are disclosed. The systems and methods can monitor a cloud-based voice over internet protocol (VoIP) calling system to determine an active call. The systems and methods can also analyze the active call to determine an indication of call quality, the analyzing occurring during the active call. Additionally, the systems and methods can compare the indication of call quality to a quality threshold. The compare can occur during the active call to determine when the active call has a poor call quality. The systems and methods can also report the poor call quality based on the comparing the indication of call quality to the quality threshold.

The present disclosure provides methods and systems that may be used for providing quality control for audio samples. The audio samples may be speech samples of a user. The user may be participating in an audio interview.

A conferencing system terminal device includes a display, an audio output, a user interface, a communication device, and one or more processors. The one or more processors present an audio difficulties user actuation target upon the display during an audio or video conference occurring across a network and concurrently with a presentation of conference content. Actuation of the audio difficulties user actuation target indicates that audio content associated with the audio or video conference being delivered by the audio output is impaired.

In an approach to generating a digital sentiment signature to characterize an end to a communication, one or more computer processors detect a start of a communication between at least two participants. A computer starts a digital timer of the communication. A computer identifies one or more digital marks of the communication, where the one or more digital marks are a reflection of a sentiment of at least one of the at least two participants in the communication. A computer generates a digital sentiment signature based on the digital timer and on the one or more digital marks, where the digital sentiment signature is a digital signal that can be communicated across a plurality of types of communication channels. A computer detects an end of the communication. A computer determines a reason for the end of the communication. A computer stores the reason.

Disclosed is a method of determining call quality for a contact centre. A call is initialised between a customer agent and a contact centre agent, with the customer agent connecting to the call at a point of presence having a selectable geographic location. Monitoring information is received, for the call, measured at the customer agent, the contact centre agent and a contact centre service provider connecting the call between the customer agent and the contact centre agent. Quality of the call is determined from the monitoring information collected from the customer agent, the contact centre agent and the contact centre service provider.

A communication system for processing a call includes control logic and at least one machine learning model generating call classifiers from outputs of an audio signal processor and a natural language processor operated on the call. Heuristic logic transforms the call classifiers into weighted sub-metrics for the call, and aggregate normalized Gaussian logic transforms the weighted sub-metrics into a metric control that may be applied as a feedback signal to adapt the operation of the control logic. The control logic in turn may adapt the behavior of an agent, automated voice attendant, or a template utilized in a call flow.

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.

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.

A computer-implemented method includes generating at least one call detail record for each call initiated on a communication network, aggregating the call detail records generated over a predetermined time period to provide an aggregated call detail record, and calculating a call set up failure rate for the calls from the aggregated call detail record, wherein the call set up failure rate is a measure of the amount of calls that failed prior to ringing at a called party's device. The computer-implemented method further includes adjusting parameters of the communication network when the call set up failure rate is above a threshold.

A method, computer system, and a computer program product for customer representative ratings is provided. The present invention may include receiving a chat transcript with one or more tagged triplets and one or more multi-dimensional success vectors. The present invention may include aggregating the one or more multi-dimensional success vectors. The present invention may include receiving at least one business priority. The present invention may include applying at least one filter to the one or more multi-dimensional success vectors. The present invention may include normalizing the one or more multi-dimensional success vectors based on the at least one applied filter. The present invention may include obtaining a rating.