GONG.io Ltd.

A system and method for training a machine learning model using sample labeling. A method includes embedding a first plurality of audio samples into a plurality of embeddings, wherein each embedding is a feature representation of a respective portion of the first plurality of audio samples; grouping the plurality of embeddings into a plurality of groups; selecting a second plurality of audio samples to be labeled based on the plurality of groups, wherein the second plurality of audio samples is selected based on a plurality of confidence scores for the plurality of groups and based on an embedding space coverage for each of the plurality of groups; and labeling the selected second plurality of audio samples by applying an automated speech recognition model to the second plurality of audio samples.

A system and method for training a machine learning model using sample labeling. A method includes embedding a first plurality of audio samples into a plurality of embeddings, wherein each embedding is a feature representation of a respective portion of the first plurality of audio samples; grouping the plurality of embeddings into a plurality of groups; selecting a second plurality of audio samples to be labeled based on the plurality of groups, wherein the second plurality of audio samples is selected based on a plurality of confidence scores for the plurality of groups and based on an embedding space coverage for each of the plurality of groups; and labeling the selected second plurality of audio samples by applying an automated speech recognition model to the second plurality of audio samples.

A system and method for verifying audio processing pipeline results using automated speech recognition. Language identification is performed on audio content in order to identify languages being spoken in the audio content. Selections related to ASR, such as selecting the ASR models or the preprocessing steps to use for ASR, are made based on the identified languages. One or more ASR models are applied in an ASR process. Results related to ASR such as the languages used for ASR selections, the selected models, or the selected preprocessing steps, are verified based on outputs of the ASR models. The verification results may be used for subsequent processing, for example, by deciding whether ASR results should be used for subsequent audio processing.

Systems, methods and programmed products for using visual information in a video stream of a recording streaming teleconference among a plurality of participants to diarize speech, involving obtaining respective components of the teleconference including a respective audio component, a respective video component, respective teleconference metadata, and transcription data, parsing components into speech segments, tagging speech segments with source feeds, and diarizing the teleconference so as to label the speech segments based on neural network or heuristic analysis of visual information.

A system and method for audio processing. A method includes tuning hyperparameters of an acoustic model based on outputs of a language identification (LID) model for a training audio data set and outputs of the acoustic model for the training audio data set; applying the LID model to a first set of features extracted from a processing audio data set in order to produce outputs of the LID model for the processing audio data set; and applying the acoustic model to a second set of features extracted from the processing audio data set and the outputs of the LID model in order to produce outputs of the acoustic model for the processing audio data set.

A method for audio processing includes receiving a recording of a teleconference among multiple participants over a network, including an audio stream containing speech uttered by the participants and information outside the audio stream. The method further includes processing the audio stream to identify speech segments interspersed with intervals of silence, extracting speaker identifications from the information outside the audio stream in the received recording, labeling a first set of the identified speech segments from the audio stream with the speaker identifications, extracting acoustic features from the speech segments in the first set, learning a correlation between the speaker identifications labelled to the segments in the first set and the extracted acoustic features, and labeling a second set of the identified speech segments using the learned correlation, to indicate the participants who spoke during the speech segments in the second set.

Systems, methods and programmed products for using visual information in a video stream of a recording streaming teleconference among a plurality of participants to diarize speech, involving obtaining respective components of the teleconference including a respective audio component, a respective video component, respective teleconference metadata, and transcription data, parsing components into speech segments, tagging speech segments with source feeds, and diarizing the teleconference so as to label the speech segments based on neural network or heuristic analysis of visual information.

Methods for uniquely identifying respective participants in a teleconference involving obtaining components of the teleconference including an audio component, a video component, teleconference metadata, and transcription data, parsing components into plural speech segments, tagging respective speech segments with speaker identification information, and diarizing the teleconference so as to label respective speech segments.

Systems, methods and programmed products for using visual information in a video stream of a recording streaming teleconference among a plurality of participants to diarize speech, involving obtaining respective components of the teleconference including a respective audio component, a respective video component, respective teleconference metadata, and transcription data, parsing components into speech segments, tagging speech segments with source feeds, and diarizing the teleconference so as to label the speech segments based on neural network or heuristic analysis of visual information.

A system and method for audio processing. A method includes training an acoustic model over a plurality of training iterations by, at each of the plurality of training iterations: applying the acoustic model to features extracted from training audio data in order to output a set of acoustic model predictions; applying a language model to at least a set of key term sample data in order to output a set of language model predictions, wherein the key term sample data demonstrates use of a plurality of key terms; clipping the training audio data into a plurality of clips based on the acoustic model predictions and the language model predictions; and tuning the acoustic model via a machine learning algorithm using the plurality of clips.