In the field of telecommunications, methods, systems, and tangible, non-transitory computer-readable mediums comprising program code are disclosed that comprise receiving a first agent, a second agent, a third agent, and a fourth agent available for pairing to a contact; and selecting the first agent for pairing to the contact based on a pairing strategy, wherein the pairing strategy is configured such that if the third agent and fourth agent had not been available, the second agent would have been selected for pairing to the contact, wherein the pairing strategy is configured such that if the first agent had not been available, the third agent would have been selected for pairing to the contact.

The present disclosure relates to automatically correcting mispronounced keywords during a conference session. More particularly, the present invention provides methods and systems for automatically correcting audio data generated from audio input having indications of mispronounced keywords during an audio/videoconferencing system. In some embodiments, the process of automatically correcting the audio data may require a re-encoding process of the audio data at the conference server. In alternative embodiments, the process may require updating the audio data at the receiver end of the conferencing system.

A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

Method and system for group communication across electronic mail users and feature phone users. A method for receiving electronic mails on a feature phone includes receiving a text message on the feature phone in response to a first electronic mail. The method also includes calling a telephonic number embedded in the text message to listen to an audio clip of the first electronic mail and providing a voice input as a response to the first electronic mail. The system includes a feature phone that receives a plurality of electronic mails. The system also includes one or more electronic devices, and a communication interface in electronic communication with the feature phone and the one or more electronic devices. The system further includes a memory that stores instructions, and a processor responsive to the instructions to receive a text message in response to an electronic mail transmitted by an electronic mail user.

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.

In order to provide effective communications, the individuals engaged in the communication should have appropriately matched language proficiencies. By ensuring that a customer of a contact center is matched with an agent having, or presenting, content of a communication utilizing language proficiency appropriate for the customer, effective communications may be provided. Should an agent deviate and provide communication content having language proficiency that is misaligned with the customer, automatic corrective action may be taken to realign the language proficiency presented to the customer.

A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.

A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.

A language proficiency analyzer automatically evaluates a person's language proficiency by analyzing that person's oral communications with another person. The analyzer first enhances the quality of an audio recording of a conversation between the two people using a neural network that automatically detects loss features in the audio and adds those loss features back into the audio. The analyzer then performs a textual and audio analysis on the improved audio. Through textual analysis, the analyzer uses a multi-attention network to determine how focused one person is on the other and how pleased one person is with the other. Through audio analysis, the analyzer uses a neural network to determine how well one person pronounced words during the conversation.

In order to solve a conventional problem that there is no interpretation system realized by a server apparatus and one or more terminal apparatuses, and configured to distribute, to one or more users, one or more pieces of interpreted speech obtained from speech of one speaker through interpretation performed by one or more interpreters, wherein the server apparatus properly manages information regarding languages of one or more interpreters, a server apparatus includes an interpreter information group storage unit in which an interpreter information group, which is a group of one or more pieces of interpreter information, is stored, the information being information regarding an interpreter who interprets speech in a first language to a second language, and having a first language identifier for identifying the first language, a second language identifier for identifying the second language, and an interpreter identifier for identifying the interpreter. Accordingly, it is possible to properly manage information regarding languages of one or more interpreters.