Speech-to-text engine customization
AssigneeInternational Business Machines Corporation
InventorsRaghu Kiran Ganti, Shreeranjani Srirangamsridharan, Mudhakar Srivatsa, Dakshi Agrawal
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Abstract
Systems, methods, and computer-readable media are described for automatically identifying potential errors in the text output of a domain-agnostic speech-to-text engine and generating text snippets that contain words representative of the potential errors and other words in the neighborhoods of such words for context. In this manner, a substantially reduced amount of text (i.e., the text snippets) can be reviewed for errors in the speech-to-text conversion rather than the entire text output, thereby significantly reducing the burden associated with error identification in the text output.
Claims20
A computer-implemented method for automated identification of one or more potential errors in a text output of a speech-to-text engine, the method comprising:
- receiving, using a processor, the text output of the speech-to-text engine;
- determining, using the processor, a first vector representation of a first word in the text output;
- determining, using the processor, a second vector representation of a second word in the text output;
- determining, using the processor, that the first vector representation and the second vector representation satisfy a similarity threshold;
- determining that the first word and the second word form a synonym cluster based at least in part on determining that the first vector representation and the second vector representation satisfy the similarity threshold, wherein the synonym cluster is indicative of a potential error in the text output;
- and
- generating a text snippet from the text output, wherein the text snippet comprises at least the first word and the second word.
A system for automated identification of one or more potential errors in a text output of a speech-to-text engine, the system comprising:
- at least one processor;
- and
- at least one memory storing computer-executable instructions, wherein the at least one processor is configured to access the at least one memory and execute the computer-executable instructions to:
- receive the text output of the speech-to-text engine;
- determine a first vector representation of a first word in the text output;
- determine a second vector representation of a second word in the text output;
- determine that the first vector representation and the second vector representation satisfy a similarity threshold;
- determine that the first word and the second word form a synonym cluster based at least in part on determining that the first vector representation and the second vector representation satisfy the similarity threshold, wherein the synonym cluster is indicative of a potential error in the text output;
- and
- generate a text snippet from the text output, wherein the text snippet comprises at least the first word and the second word.
A computer program product for automated identification of one or more potential errors in a text output of a speech-to-text engine, the computer program product comprising a storage medium readable by a processing circuit, the storage medium storing instructions executable by the processing circuit to cause the processing circuit to perform a method comprising:
- receiving the text output of the speech-to-text engine;
- determining a first vector representation of a first word in the text output;
- determining a second vector representation of a second word in the text output;
- determining that the first vector representation and the second vector representation satisfy a similarity threshold;
- determining that the first word and the second word form a synonym cluster based at least in part on determining that the first vector representation and the second vector representation satisfy the similarity threshold, wherein the synonym cluster is indicative of a potential error in the text output;
- and
- generating a text snippet from the text output, wherein the text snippet comprises at least the first word and the second word.
Dependencies are inferred from the claim text.