Metric learning of speaker diarization
AssigneeInternational Business Machines Corporation
InventorsMasayuki Suzuki, Yosuke Higuchi
Life of this patent
Estimating the term…
Abstract
A computer-implemented method includes obtaining training data including utterances of speakers in acoustic conditions, preparing at least one machine learning model, each machine learning model including a common embedding model for converting an utterance into a feature vector and a classification model for classifying the feature vector, and training, by using the training data, the machine learning model to perform classification by speaker and to perform classification by acoustic condition.
Claims20
A computer-implemented method comprising:
- obtaining training data including a plurality of utterances of a plurality of speakers in a plurality of acoustic conditions;
- preparing at least one machine learning model, each machine learning model including a common embedding model for converting an utterance into a feature vector and a classification model for classifying the feature vector;
- and
- training, by using the training data, the at least one machine learning model to perform classification by speaker and to perform classification by acoustic condition.
A computer program product including one or more computer-readable storage mediums collectively storing program instructions that are executable by a processor or programmable circuitry to cause the processor or programmable circuitry to perform operations comprising:
- obtaining training data including a plurality of utterances of a plurality of speakers in a plurality of acoustic conditions;
- preparing at least one machine learning model, each machine learning model including a common embedding model for converting an utterance into a feature vector and a classification model for classifying the feature vector;
- and
- training, by using the training data, the at least one machine learning model to perform classification by speaker and to perform classification by acoustic condition.
An apparatus comprising:
- a processor or a programmable circuitry;
- and
- one or more computer-readable mediums collectively including instructions that, when executed by the processor or the programmable circuitry, cause the processor or the programmable circuitry to:
- obtain training data including a plurality of utterances of a plurality of speakers in a plurality of acoustic conditions;
- prepare at least one machine learning model, each machine learning model including a common embedding model for converting an utterance into a feature vector and a classification model for classifying the feature vector;
- and
- train by using the training data, the at least one machine learning model to perform classification by speaker and to perform classification by acoustic condition.
Dependencies are inferred from the claim text.