US 2026/0294333 A1 · METHODS AND SYSTEMS FOR CLASSIFYING SPEECH DATA

Abstract

A non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to receive and provide first speech data as input to a context window of a first machine learning model to generate a first assessment metric value associated with a first assessment aspect from a plurality of assessment aspects. The instructions further cause the processor to receive second speech data. The second speech data and data from the context window of the first machine learning model are provided to a context window of a second machine learning model to generate a second assessment metric value associated with a second assessment aspect from the plurality of assessment aspects. An assessment outcome is determined based on the first assessment metric value and the second assessment metric value, and the instructions cause the processor to cause display at a user compute device of an indication of the assessment outcome.

Claims20

Dependent claims

A non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to:

receive first speech data;
provide the first speech data as input to a context window of a first machine learning model to generate a first assessment metric value associated with a first assessment aspect from a plurality of assessment aspects;
receive second speech data;
provide the second speech data and data from the context window of the first machine learning model to a context window of a second machine learning model to generate a second assessment metric value associated with a second assessment aspect from the plurality of assessment aspects;
determine an assessment outcome based on the first assessment metric value and the second assessment metric value;
and
cause display at a user compute device of an indication of the assessment outcome.

4 more elements

A method, comprising:

causing, via a processor, a first question associated with a first assessment aspect from a plurality of assessment aspects to be presented to a user via a user compute device;
after causing the first question to be presented, receiving, at the processor, first speech data that represents a response of the user to the first question;
providing, via the processor, the first speech data as input to a context window of a first machine learning model to generate a first assessment metric value associated with the first assessment aspect;
causing, via the processor, a second question associated with a second assessment aspect from the plurality of assessment aspects to be presented to the user via the user compute device;
after causing the second question to be presented, receiving, at the processor, second speech data that represents a response of the user to the second question;
providing, via the processor, the second speech data and data from the context window of the first machine learning model to a context window of a second machine learning model to generate a second assessment metric value associated with the second assessment aspect;
determining, via the processor, an assessment outcome based on the first assessment metric value and the second assessment metric value;
and
causing, via the processor, an indication of the assessment outcome to be presented to the user via the user compute device.

6 more elements

A non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to:

provide first speech data associated with a user as input to a context window of a first machine learning model to generate a first assessment metric value associated with a first assessment aspect from a plurality of assessment aspects;
provide (1) second speech data associated with the user and (2) data from the context window of the first machine learning model to a context window of a second machine learning model to generate a second assessment metric value associated with a second assessment aspect from the plurality of assessment aspects;
receive a data structure representing at least one of a population atemporal condition, a subject temporal condition, or a condition comparison through time;
at least one of:
provide the data structure as input to the context window of the first machine learning model to generate a modified first assessment metric value, or
provide the data structure as input to the context window of the second machine learning model to generate a modified second assessment metric value;
determine an assessment outcome based on at least one of the first assessment metric value, the second assessment metric value, the modified first assessment metric value, or the modified second assessment metric value;
and
cause display at a user compute device of an indication of the assessment outcome.

6 more elements

Dependencies are inferred from the claim text.

Methods and systems for classifying speech data

Life of this patent

Methods and systems for classifying speech data

Life of this patent

Abstract

Claims20