SYSTEM AND METHOD OF AUTOMATED EVALUATION OF TRANSCRIPTION QUALITY

• US 20180068651A1
• Filed: 08/14/2017
• Published: 03/08/2018
• Est. Priority Date: 07/30/2013
• Status: Active Grant

• Current Assignee: Verint Systems Incorporated
• Original Assignee: Verint Systems Limited (Verint Systems Incorporated)
• Inventors: Sidi, Oana, Wein, Ron
• Granted Patent: US 10,147,418 B2
• Time in Patent Office: Days
• CPC Class Codes: G10L 15/01 Assessment or evaluation of speech recognition systems
• CPC Class Codes: G10L 15/04 Segmentation; Word boundary detection
• CPC Class Codes: G10L 15/12 using dynamic programming techniques, e.g. dynamic time warping [DTW]
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence

Abstract

Systems and methods automatedly evaluate a transcription quality. Audio data is obtained. The audio data is segmented into a plurality of utterances with a voice activity detector operating on a computer processor. The plurality of utterances are transcribed into at least one word lattice with a large vocabulary continuous speech recognition system operating on the processor. A minimum Bayes risk decoder is applied to the at least one word lattice to create at least one confusion network. At least conformity ratio is calculated from the at least one confusion network.

Claims

1. A method of automated evaluation of a transcription quality, the method comprising:

obtaining audio data;

segmenting the audio data into a plurality of utterances with a voice activity detector operating on a computer processor;

transcribing the plurality of utterances into at least one word lattice with a large vocabulary continuous speech recognition system operating on the processor;

applying, by the processor, a minimum Bayes risk decoder to the at least one word lattice to create at least one confusion network representing the at least one word lattice as a plurality sequential word bins and c-bins; and

calculating, by the processor, at least one conformity ratio from the least one confusion network.

2. The method of claim 1, wherein the audio data is streaming audio data.

21. The method of claim 1, wherein calculating the at least one conformity ratio for the at least one confusion network further comprises:

identifying a probability value of a most probable word arc in each word bin; and

calculating a joint probability for each c-bin and a preceding word bin;

wherein the at least one conformity ratio is an average of the calculated joint probabilities for the at least one confusion network.

22. The method of claim 1, further comprising calculating a transcription quality score from the at least one conformity ratio.

23. The method of claim 22, wherein the transcription quality score is a normalized value of the at least one conformity ratio.

24. The method of claim 1, wherein each of the plurality of utterances is transcribed into a word lattic.

25. The method of claim 24, further comprising calculating an overall conformity ratio for a transcription of the audio data from the conformity ratios calculated from the confusion network of each of the utterances in the plurality of utterances.

26. The method of claim 22, further comprising producing an indication of the of the transcription quality score.

27. The method of claim 24, wherein transcribing the plurality of utterances comprises applying at least one transcription model to each of the plurality of utterances and wherein the at least one conformity ratio is indicative of a conformity between the audio data and the at least one transcription model.

28. The method of claim 27, further comprising:

selecting a new at least one transcription model based upon the at least one conformity ratio; and

transcribing the plurality of utterances by applying the new at least one transcription model to each of the plurality of utterances.

3-20. -20. (canceled)

29. A system for automated evaluation of transcription quality, the system comprising:

an audio data source upon which a plurality of audio data files are stored;

a processor that receives the plurality of audio data files, segments the audio data files into a plurality of utterances and applies at least one transcription model to the plurality of utterances to transcribe the plurality of utterances into at least one word lattice; and

a non-transient computer readable medium communicatively connected to the processor and programmed with computer readable code that when executed by the processor causes the processor to;

apply a minimum Bayes risk decoder to the at least one word lattice to create at least one confusion network representing the at least one word lattice as a plurality of sequential word bins and c-bins; and

calculate at least one conformity ratio from the at least one confusion network.

30. The system of claim 29, wherein the audio data source is a streaming audio data source.

31. The system of claim 29, wherein the computer readable code to calculate the at least one conformity ratio for the at least one confusion network further comprises code that when executed by the processor causes the processor to:

identify a probability value of a most probable word arc in each word bin; and

calculate a joint probability for each c-bin and a preceding word bin;

wherein the at least one conformity ratio is an average of the calculated joint probabilities for the at least one confusion network.

32. The system of claim 29, wherein the computer readable code further comprises code that when executed by the processor causes the processor to:

calculate a transcription quality score from the at least one conformity ratio.

33. The system of claim 32, wherein the transcription quality score is a normalized value of the at least one conformity ratio.

34. The system of claim 29, wherein each of the plurality of utterances is transcribed into a word lattic.

35. The system of claim 34, wherein the computer readable code further comprises code that when executed by the processor causes the processor to:

calculate an overall conformity ratio for a transcription of the audio data from the conformity ratios calculated from the confusion network of each of the utterances in the plurality of utterances.

36. The system of claim 32, wherein the computer readable code further comprises code that when executed by the processor causes the processor to:

produce an indication of the of the transcription quality score.

37. The method of claim 34, wherein the computer readable code further comprises code that when executed by the processor causes the processor to:

apply at least one transcription model to each of the plurality of utterances and wherein the at least one conformity ratio is indicative of a conformity between the audio data and the at least one transcription model.

38. The method of claim 37, wherein the computer readable code further comprises code that when executed by the processor causes the processor to:

select a new at least one transcription model based upon the at least one conformity ratio; and

transcribe the plurality of utterances by applying the new at least one transcription model to each of the plurality of utterances.