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US 10,311,859 B2Granted
Material selection for language model customization in speech recognition for speech analytics
OwnerAmir Lev-Tov, Avraham Faizakof +2inventors
InventorsAmir Lev-Tov, Avraham Faizakof, Tamir Tapuhi, Yochai Konig
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Abstract
A method for extracting, from non-speech text, training data for a language model for speech recognition includes: receiving, by a processor, non-speech text; selecting, by the processor, text from the non-speech text; converting, by the processor, the selected text to generate converted text comprising a plurality of phrases consistent with speech transcription text; training, by the processor, a language model using the converted text; and outputting, by the processor, the language model.
Claims18
A method for customizing a language model for speech recognition in a context, the method comprising:
- receiving, by a processor, non-speech text from the context, the context comprising communications with an enterprise, the communications comprising voice interactions and non-speech communications;
- selecting, by the processor, text from the non-speech text;
- converting, by the processor, the selected non-speech text to generate converted non-speech text comprising a plurality of phrases consistent with speech transcription text;
- customizing, by the processor, a language model for the context using the converted non-speech text, the language model being customized to compute a probability that a given speech input phrase appears in voice interactions in the context of the communications with the enterprise;
- and
- outputting, by the processor, the language model.
3 more elements
A method for selecting, from non-speech text, training data for a language model for speech recognition, the method comprising:
- training, by a processor, a non-speech language model based on the non-speech text;
- for each unique sentence of the non-speech text:
- computing and normalizing, by the processor, an out-of-domain score of the unique sentence based on non-speech language model;
- computing and normalizing, by the processor, an in-domain score of the unique sentence based on a speech transcription language model trained based on generic speech transcription training data;
- comparing, by the processor, the out-of-domain score to the in-domain score;
- and
- adding, by the processor, the unique sentence to an output set of selected text in response to determining that the in-domain score exceeds the out-of-domain score by a threshold;
- and
- outputting, by the processor, the output set of selected text.
6 more elements
A method for selecting, from non-speech text, training data for a language model for speech recognition, the method comprising:
- initializing, by a processor, an output set of selected text based a plurality of sentences sampled from the non-speech text;
- for each unique sentence of the non-speech text:
- computing, by the processor, a first divergence between an in-domain language model trained on generic speech transcript text the unique sentence and a language model trained on the output set;
- computing, by the processor, a second divergence between the in-domain language model and a language model trained on the output set combined with the unique sentence;
- comparing, by the processor, the first divergence and the second divergence;
- and
- adding, by the processor, the sentence to the output set in response to determining that the second divergence in less than the first divergence;
- and
- outputting, by the processor, the output set of selected text.
6 more elements
A system comprising:
- a processor;
- memory storing instructions that, when executed by the processor, cause the processor to:
- receive non-speech text from a context comprising communications with an enterprise, the communications comprising voice interactions and non-speech communications;
- select text from the non-speech text;
- convert the selected non-speech text to generate converted non-speech text comprising a plurality of phrases consistent with speech transcription text;
- customize a language model for the context using a converted non-speech text, the language model being customized to compute a probability that a given speech input phrase appears in voice interactions in the context of the communications with the enterprise;
- and
- output the language model.
5 more elements
A system comprising:
- a processor;
- and
- memory storing instructions that, when executed by the processor, cause the processor to:
- train a non-speech language model based on the non-speech text;
- for each unique sentence of the non-speech text:
- compute and normalize an out-of-domain score of the unique sentence based on non-speech language model;
- compute and normalize an in-domain score of the unique sentence based on a speech transcription language model trained based on generic speech transcription training data;
- compare the out-of-domain score to the in-domain score;
- and
- add the unique sentence to an output set of selected text in response to determining that the in-domain score exceeds the out-of-domain score by a threshold;
- and
- output the output set of selected text.
9 more elements
A system comprising:
- a processor;
- and
- memory storing instructions that, when executed by the processor, cause the processor to:
- initialize an output set of selected text based a plurality of sentences sampled from the non-speech text;
- for each unique sentence of the non-speech text:
- compute a first divergence between an in-domain language model trained on generic speech transcript text the unique sentence and a language model trained on the output set;
- compute a second divergence between the in-domain language model and a language model trained on the output set combined with the unique sentence;
- compare the first divergence and the second divergence;
- and
- add the sentence to the output set in response to determining that the second divergence in less than the first divergence;
- and
- output the output set of selected text.
9 more elements
Dependencies are inferred from the claim text.