Metric learning of speaker diarization
AssigneeInternational Business Machines Corporation
InventorMasayuki Suzuki
Life of this patent
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Abstract
A computer-implemented method includes obtaining, using a hardware processor, training data including utterances of speakers and performing tasks to train a machine learning model that converts an utterance into a feature vector, each task using one subset of multiple subsets of training data. The subsets of training data include a first subset of training data including utterances of a first number of speakers and at least one second subset of training data. Each second subset of training data includes utterances of a number of speakers that is less than the first number of speakers.
Claims19
A computer-implemented method comprising:
- obtaining, using a hardware processor, training data stored on one or more computer readable storage mediums, the training data including a plurality of utterances of a plurality of speakers;
- and
- performing, using the hardware processor, a plurality of tasks to train a machine learning model that converts an utterance of the plurality of utterances into a feature vector, each task using one of a plurality of subsets of training data, wherein the plurality of subsets of training data includes:
- a first subset of training data including utterances of a first number of speakers among the plurality of speakers, and
- at least one second subset of training data, each second subset including utterances of a number of speakers among the plurality of speakers that is less than the first number of speakers among the plurality of speakers,
- wherein performing the plurality of tasks to train the machine learning model includes performing one task using the first subset of training data and two or more tasks using the at least one second subset of training data.
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;
- and
- performing a plurality of tasks to train a machine learning model that converts an utterance of the plurality of utterances into a feature vector, each task using one of a plurality of subsets of training data, wherein the plurality of subsets of training data includes:
- a first subset of training data including utterances of a first number of speakers among the plurality of speakers, and
- at least one second subset of training data, each second subset including utterances of a number of speakers among the plurality of speakers that is less than the first number of speakers among the plurality of speakers,
- wherein performing the plurality of tasks to train the machine learning model includes performing one task using the first subset of training data and two or more tasks using the at least one second subset of training data.
An apparatus comprising:
- a processor or programmable circuitry;
- and
- one or more computer readable storage 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;
- and
- perform a plurality of tasks to train a machine learning model that converts an utterance of the plurality of utterances into a feature vector, each task using one of a plurality of subsets of training data, wherein the plurality of subsets of training data includes:
- a first subset of training data including utterances of a first number of speakers among the plurality of speakers, and
- at least one second subset of training data, each second subset including utterances of a number of speakers among the plurality of speakers that is less than the first number of speakers among the plurality of speakers,
- wherein performing the plurality of tasks to train the machine learning model includes performing one task using the first subset of training data and two or more tasks using the at least one second subset of training data.
Dependencies are inferred from the claim text.