Material selection for language model customization in speech recognition for speech analytics

Patent metadata

Field	Value
Publication number	US-10311859-B2
Application number	US-201615247656-A
Country	US
Kind code	B2
Filing date	Aug 25, 2016
Priority date	Jan 16, 2016
Publication date	Jun 4, 2019
Grant date	Jun 4, 2019

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.

First claim

What is claimed is:

1. 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.

2. The method of claim 1 , wherein the non-speech text comprises at least one from the group consisting of:
an email;
a forum post;
a transcript of a text chat interaction; or
a text message.

3. The method of claim 1 , wherein the converting the selected non-speech text comprises:
removing metadata from the non-speech text;
splitting the non-speech text into a plurality of sentences;
converting one or more words of the sentences to spoken form;
correcting one or more spelling errors in the sentences;
identifying one or more duplicate sentences; and
removing duplicate sentences.

4. The method of claim 1 , wherein the selecting the text comprises:
for each in-vocabulary word in a lexicon of in-vocabulary words,
identifying one or more sentences containing the in-vocabulary word;
counting the one or more sentences to identify a count of the in-vocabulary word in the non-speech text;
comparing the count to a first threshold; and
adding the identified one or more sentences containing the in-vocabulary word in response to determining that the count satisfies the first threshold;
identifying one or more out-of-vocabulary words comprising words that are in the sentences and not in the lexicon;
for each out-of-vocabulary word of the out-of-vocabulary words:
identifying one or more sentences containing the out-of-vocabulary word;
counting the one or more sentences to identify a count of the out-of-vocabulary word in the non-speech text;
comparing the count to a second threshold;
computing a first likelihood of encountering the out-of-vocabulary word in the sentence among all of the identified sentences;
identifying one or more spelling suggestions for the out-of-vocabulary word;
computing a plurality of second likelihoods, each of the second likelihoods corresponding to a second likelihood of encountering each of the spelling suggestions in the sentence;
adding the identified sentences to an output set of selected text in response to determining that the count satisfies a threshold and that at all of the second likelihoods are less than the first likelihood; and

outputting the output set of selected text.

5. The method of claim 4 , wherein the computing the first likelihood comprises counting occurrences of the out-of-vocabulary word preceded by one or more history words in the non-speech text; and
wherein the computing one of the second likelihoods comprises counting occurrences of a corresponding spelling suggestion of the spelling suggestions preceded by the one or more history words in the non-speech text.

6. 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.

7. The method of claim 6 , further comprising scaling a count of each unique sentence in the output set by P(s), where:
P ( s )= e IDScr′
where s is the unique sentence and where IDScr′ is the in-domain score of the unique sentence.

8. 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.

9. The method of claim 8 , wherein the computing the second divergence comprises calculating a cross-entropy of the in-domain language model and the language model trained on the output set.

10. 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.

11. The system of claim 10 , wherein the non-speech text comprises-at least one from the group consisting of:
an email;
a forum post;
a transcript of a text chat interaction; or
a text message.

12. The system of claim 10 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to convert the selected non-speech text by:
removing metadata from the non-speech text;
splitting the non-speech text into a plurality of sentences;
converting one or more words of the sentences to spoken form;
correcting one or more spelling errors in the sentences;
identifying one or more duplicate sentences; and
removing duplicate sentences.

13. The system of claim 10 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to select the text by:
for each in-vocabulary word in a lexicon of in-vocabulary words,
identifying one or more sentences containing the in-vocabulary word;
counting the one or more sentences to identify a count of the in-vocabulary word in the non-speech te

Assignees

• Genesys Telecommunications Laboratories Inc

Inventors

• Lev-Tov Amir
• Faizakof Avraham
• Tapuhi Tamir
• Konig Yochai

Classifications

• G10L15/183

  using context dependencies, e.g. language models · CPC title

• G10L15/26

  Speech to text systems (G10L15/08 takes precedence) · CPC title

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title

• G10L2015/0636

  Threshold criteria for the updating · CPC title

• G06F40/232

  Orthographic correction, e.g. spell checking or vowelisation · CPC title