Abstract: A system comprises a memory communicatively coupled to at least one processor. The processor is configured to obtain audio data from a user device configured to perform one or more communication operations with a workspace device. In response to receiving the audio data, the processor is configured to execute the machine learning algorithm to transcribe the audio data into text data and summarize the text data into a request summary. Further, the processor is configured to determine a target operation based on the request summary. The target operation is a determined intent to perform a communication operation. The processor is configured to determine whether the communication operation at least partially matches the authorized communication operations and present the request summary as a reset point to train the one or more machine learning models in response to determining that the communication operation at least partially matches the authorized communication operations.

Abstract: A system comprises a memory communicatively coupled to at least one processor. The memory is operable to store a machine learning algorithm configured to evaluate data in accordance with one or more machine learning models. The at least one processor is configured to obtain audio data from a user device. In response to receiving the audio data, the processor is configured to execute the machine learning algorithm to transcribe the audio data into text data and summarize the text data into a request summary. Further, the processor is configured to determine a target operation based on the request summary in response to summarizing the text data. The target operation is a determined intent to perform a communication operation. The processor is configured to map the target operation to a suggestion and presenting the suggestion to a workspace device.

Abstract: Methods and systems for transcribing communications are provided. Methods may include receiving a communication. Methods may include splitting the communication into a plurality of communication segments. Each communication segment may include two or more words. Methods may include transcribing each segment included in the plurality of communication segments, in parallel. The transcribing may include using a transformer neural network to transcribe each segment included in the plurality of communication segments. Methods may include generating a transcription from the transcribing. The transcription may be generated by combining the transcription of each of the communication segments into a combined transcription. Methods may include correcting the combined transcription.

Abstract: Methods and systems for transcribing communications are provided. Methods may include receiving a communication. Methods may include splitting the communication into a plurality of communication segments. Each communication segment may include two or more words. Methods may include transcribing each segment included in the plurality of communication segments, in parallel. The transcribing may include using a transformer neural network to transcribe each segment included in the plurality of communication segments. Methods may include generating a transcription from the transcribing. The transcription may be generated by combining the transcription of each of the communication segments into a combined transcription. Methods may include correcting the combined transcription.

Abstract: An interactive voice response system for use with a real-time complaint identification system and dashboard may be provided. The interactive voice response system may receive and respond to human callers in real-time. Simultaneously, the interactive voice response system may use a model to transcribe the audio call and identify complaints within the audio calls. Identified complaints may be resolved by one of a plurality of resolution executables. One of the resolution executables may include registering the identified complaint with a complaint register.

Abstract: Methods and systems for transcribing communications are provided. Methods may include receiving a communication. Methods may include splitting the communication into a plurality of communication segments. Each communication segment may include two or more words. Methods may include transcribing each segment included in the plurality of communication segments, in parallel. The transcribing may include using a transformer neural network to transcribe each segment included in the plurality of communication segments. Methods may include generating a transcription from the transcribing. The transcription may be generated by combining the transcription of each of the communication segments into a combined transcription. Methods may include correcting the combined transcription.

Abstract: A method for training a model is provided. Methods may, at a first pre-step, receive a base model. Methods may, at a first step, create a label using the base model to be applied to unlabeled data, label a first set of unlabeled data with the label and generate a first set of labeled data from the first set of unlabeled data. Methods may, at a second step, receive a second set of labeled data, separate the second set into a first group and a second group, classify the first group and the first set as training data. Methods may, at a third step, train a deep learning model using the training data, validate the deep learning model using the second group and generate a set of model parameters. Methods may, at a fourth step, save the model parameters. Methods may, at a fifth step, train the deep learning, initialized from the parameters, using the first group. Methods may, at a sixth step, replace the base model with the deep learning model and repeat steps one through six.

Abstract: An interactive voice response system for use with a real-time complaint identification system and dashboard may be provided. The interactive voice response system may receive and respond to human callers in real-time. Simultaneously, the interactive voice response system may use a model to transcribe the audio call and identify complaints within the audio calls. Identified complaints may be resolved by one of a plurality of resolution executables. One of the resolution executables may include registering the identified complaint with a complaint register.