Abstract: The disclosed computer-implemented methods and systems include training a machine-learning model to accurately generate representations of similar scenes from long-form videos that have semantically different speech audio. For example, the methods and systems described herein generate machine-learning model training data including video clips and corresponding audio spectrograms. To augment this data, the methods and systems described herein further include dubbed audio spectrograms with the training data such that each video clips corresponds with a primary language audio spectrogram and a secondary language audio spectrogram. By applying a machine-learning model to this training data, the systems and methods described herein teach the machine-learning model to de-emphasize speech audio when generating audio visual representations corresponding to scenes from long-form video. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed system continuously refines a model used by an Automatic Speech Recognition (ASR) system to enable fast and accurate transcriptions of detected speech activity. The ASR system analyzes speech activity to generate text transcriptions and associated metrics (such as minimum Bayes risk and/or perplexity) that correspond to the quality of or confidence in each generated transcription. The system employs a filtering process to select certain text transcriptions based in part on one or more associated quality metrics. In addition, the system corrects for known systemic errors within the ASR system and provides a mechanism for manual review and correction of transcriptions. The system selects a subset of transcriptions based on factors including confidence score, and uses the selected subset of transcriptions to re-train the ASR model. By continuously retraining the ASR model, the system is able to provide ever faster and more accurate text transcriptions of detected speech activity.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: The disclosed system continuously refines a model used by an Automatic Speech Recognition (ASR) system to enable fast and accurate transcriptions of detected speech activity. The ASR system analyzes speech activity to generate text transcriptions and associated metrics (such as minimum Bayes risk and/or perplexity) that correspond to the quality of or confidence in each generated transcription. The system employs a filtering process to select certain text transcriptions based in part on one or more associated quality metrics. In addition, the system corrects for known systemic errors within the ASR system and provides a mechanism for manual review and correction of transcriptions. The system selects a subset of transcriptions based on factors including confidence score, and uses the selected subset of transcriptions to re-train the ASR model. By continuously retraining the ASR model, the system is able to provide ever faster and more accurate text transcriptions of detected speech activity.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.