Abstract: The methods, apparatus, non-transitory computer readable media, and systems described herein include recording a mono recording of a software and a customer inquiry communication using a microphone to interpret and respond to the customer inquiry communication, wherein the mono recording is unseparated and includes customer voice data and audio data generated by the software agent, separately and concurrently recording the software agent audio data in an agent recording, and subtracting agent audio data from the unseparated mono recording to provide a separated recording including only customer voice data.

Abstract: The methods, apparatus, non-transitory computer readable media, and systems described herein include recording a mono recording of a software and a customer inquiry communication using a microphone to interpret and respond to the customer inquiry communication, wherein the mono recording is unseparated and includes customer voice data and audio data generated by the software agent, separately and concurrently recording the software agent audio data in an agent recording, and subtracting agent audio data from the unseparated mono recording to provide a separated recording including only customer voice data.

Abstract: The methods, apparatus, non-transitory computer readable media, and systems described herein include recording a mono recording of a face-to-face communication between an agent and a customer using a microphone, wherein the mono recording is unseparated and includes agent voice data and customer voice data, separately recording the agent voice data in an agent recording using a second microphone; aligning the unseparated mono recording and the agent recording so they are time-synched; subtracting agent voice data from the unseparated mono recording using the agent recording to provide a separated recording including only customer voice data, wherein the agent voice data is subtracted from the unseparated mono recording based on the alignment, sound frequency analysis, or both; converting at least the customer voice data to text; and determining a personality type of the customer by applying one or more computer-implemented linguistic algorithms to the text of the customer voice data.

Abstract: The methods, apparatus, non-transitory computer readable media, and systems described herein include recording a mono recording of a face-to-face communication between an agent and a customer using a microphone, wherein the mono recording is unseparated and includes agent voice data and customer voice data, separately recording the agent voice data in an agent recording using a second microphone; aligning the unseparated mono recording and the agent recording so they are time-synched; subtracting agent voice data from the unseparated mono recording using the agent recording to provide a separated recording including only customer voice data, wherein the agent voice data is subtracted from the unseparated mono recording based on the alignment, sound frequency analysis, or both; converting at least the customer voice data to text; and determining a personality type of the customer by applying one or more computer-implemented linguistic algorithms to the text of the customer voice data.

Abstract: The methods, apparatus, and systems described herein analyze mono-recorded customer-agent communications to apply distress analysis techniques to identify one or more distress events. The methods include recording a mono recording of a communication between an agent and a customer, separately recording the agent voice data in an agent recording, subtracting agent voice data from the mono recording using the agent recording to provide a separated recording including only customer voice data, convening at least the customer voice data to text, and applying distress analysis to a portion of the communication to identify one or more distress events.

Abstract: The methods, apparatus, and systems described herein analyze mono-recorded customer-agent communications to apply distress analysis techniques to identify one or more distress events. The methods include recording a mono recording of a communication between an agent and a customer, separately recording the agent voice data m an agent recording, subtracting agent voice data from the mono recording using the agent recording to provide a separated recording including only customer voice data, convening at least the customer voice data to text, and applying distress analysis to a portion of the communication to identify one or more distress events.

Abstract: The methods, apparatus, and systems described herein analyze customer-agent communications to determine personality of a customer. The methods include recording a mono recording of a communication between an agent and a customer, separately recording the agent voice data in an agent recording, subtracting agent voice data from the mono recording using the agent recording to provide a separated recording including only customer voice data, converting at least the customer voice data to text, and determining a personality type of the customer by applying one or more computer-implemented linguistic algorithms to the text of the customer voice data.