Comparative ASR evaluation

Dan BurnettDirector of Speech Technologies, Voxeo

SpeechTek New YorkAugust 2010

Goals for today

• Learn about data selection

• Learn all the steps of doing an eval by actually doing them

• Leave with code that runs

Outline

• Overview of comparative ASR evaluation

• How to select an evaluation data set

• Why transcription is important and how to do it properly

• What and how to test

• Analyzing the results

Comparative ASR Evaluation

• How could you compare ASR accuracy?

• Can you test against any dataset?

• What settings should you use? The optimal ones, right?

Today’s approach• Choose representative evaluation data set

• Determine human classification of each recording

• For each ASR engine

• Determine machine classification of each recording at “optimal” setting

• Compare to human classification to determine accuracy

• Intelligently compare results for the two engines

Evaluation data set

• Ideally at least 100 recordings per grammar path for good confidence in results (up to 10000 minimum for large grammars)

• Must be representative

• Best to take from actual calls (why?)

• Do you need all the calls? Consider

• Time of day, day of week, holidays

• Regional differences

• Simplest is to use every nth call

Lab data set

• Stored in all-data

• In “original” format as recorded

• Only post-endpointed data for today

• 1607 recordings of answers to yes/no question

• Likely to contain yes/no, but not guaranteed

Transcription

• Why is it needed? Why not automatic?

• Stages

• Classification

• Transcription

Audio classification• Motivation:

• Applications may distinguish (i.e. possibly behave differently) among the following cases:

Case Possible behaviorNo speech in audio sample

(nospeech)Mention that you didn’t hear anything and ask for repeat

Speech, but not intelligible (unintelligible) Ask for repeat

Intelligible speech, but not in app grammar

(out-of-grammar speech)Encourage in-grammar speech

Intelligible speech, and within app grammar (in-grammar speech) Respond to what person said

Transcribing speech

• Words only, all lower case

• No digits

• Only punctuation allowed is apostrophe

Lab 1

• Copy yn_files.csv to yn_finaltrans.csv and edit

• For each file, append category of nospeech, unintelligible, or speech

• Example: all-data/.../utt01.wav,unintelligible

• Append transcription if speech

• Example: all-data/.../utt01.wav,speech,yes

• Transcription instructions in transcription.html

• How might you validate transcriptions?

What and how to test

• Understanding what to test/measure

• Preparing the data

• Building a test harness

• Running the test

What to test/measure

• To measure accuracy, we need

• For each data file

• the human categorization and transcription, and

• the recognizer’s categorization, recognized string, and confidence score

Preparing the data

• Recognizer needs a grammar (typically from your application)

• This grammar can be used to classify transcribed speech as In-grammar/Out-of-grammar

Lab 2

• Fix GRXML yes/no grammar in “a” directory called yesno.grxml

• Copy yn_finaltrans.csv to yn_igog.csv

• Edit yn_igog.csv and change every “yes” or “no” line to have a category of “in_grammar”(should be 756 yes, 159 no, for total of 915)

Building a test harness

• Why build a test harness? What about vendor batch reco tools?

• End-to-end vs. recognizer-only testing

• Harness should be

• generic

• customizable to different ASR engines

Lab 3

• Complete the test harness harness.php(see harness_outline.txt)

• The harness must use the “a/scripts” scripts

• A list of “missing commands” is in harness_components.txt

• Please review (examine) these scripts

• FYI, ASR engine is a/a.php -- treat as black box

Lab 4

• Now run the test harness:

• php harness.php a/scripts <data file> <rundir>

• Output will be in <rundir>/results.csv

• Compare your output to “def_results.csv”

Analyzing results

• What are the possible outcomes and errors?

• How do we evaluate errors?

Possible ASR Engine Classifications

• Silence/nospeech (nospeech)

• Reject (rejected)

• Recognize (recognized)

• What about DTMF?

Possible outcomes

nospeech rejected recognized

nospeechCorrect

classificationImproperly

rejected Incorrect

unintelligible Improperly treated as silence

Correct behavior

Assume incorrect

out-of-grammar

Improperly treated as silence

Correct behavior

Incorrect

in-grammarImproperly

treated as silenceImproperly

rejectedEither correct or incorrect

True

ASR

Possible outcomes

nospeech rejected recognized

nospeechCorrect

classificationImproperly

rejected Incorrect

unintelligible Improperly treated as silence

Correct behavior

Assume incorrect

out-of-grammar

Improperly treated as silence

Correct behavior

Incorrect

in-grammarImproperly

treated as silenceImproperly

rejectedEither correct or incorrect

True

ASRMisrecognitions

Possible outcomes

nospeech rejected recognized

nospeechCorrect

classificationImproperly

rejected Incorrect

unintelligible Improperly treated as silence

Correct behavior

Assume incorrect

out-of-grammar

Improperly treated as silence

Correct behavior

Incorrect

in-grammarImproperly

treated as silenceImproperly

rejectedEither correct or incorrect

True

ASR“Misrejections”

Possible outcomes

nospeech rejected recognized

nospeechCorrect

classificationImproperly

rejected Incorrect

unintelligible Improperly treated as silence

Correct behavior

Assume incorrect

out-of-grammar

Improperly treated as silence

Correct behavior

Incorrect

in-grammarImproperly

treated as silenceImproperly

rejectedEither correct or incorrect

True

ASR“Missilences”

Three types of errors

• Missilences -- called silence, but wasn’t

• Misrejections -- rejected inappropriately

• Misrecognitions -- recognized inappropriately or incorrectly

Three types of errors

• Missilences -- called silence, but wasn’t

• Misrejections -- rejected inappropriately

• Misrecognitions -- recognized inappropriately or incorrectly

So how do we evaluate these?

Evaluating errors

• Run ASR Engine on data set

• Try every rejection threshold value

• Plot errors as function of threshold

• Find optimal value

Try every rejection threshold value

• Ran data files through test harness with rejection threshold of 0 (i.e., no rejection), but recorded confidence score

• Now, for each possible rejection threshold from 0 to 100

• Calculate number of misrecognitions, misrejections, and missilences

Semantic equivalence

• We call “yes” in-grammar, but what about “yes yes yes”?

• Application only cares about whether it does the right thing, so

• Our final results need to be semantic results

Lab 5

• Look at synonyms.txt file

• Analyze at single threshold and look at the result

• php analyze_csv.php <csv file> 50 synonyms.txt

• Note the difference between raw and semantic results

• Now evaluate at all thresholds and look at the (semantic) results

• php analyze_all_thresholds.php <csv file> <synonyms file>

“Missilences”

Misrecognitions

“Misrejections”

ASR Engine A errors

0 100Rejection Threshold 100

500

1000

Sum

MinimumTotal Error

0 100Rejection Threshold

500

1000

ASR Engine A errors

Lab 6• You now have engine B in “b” directory

• Change harness and component scripts as necessary to run the same test

• You need to know that

• The API for engine B is different. Run “php b/b.php” to find out what it is. It takes ABNF grammars instead of XML.

• Engine B stores its output in a different file.

• Possible outputs from engine B are

• <audiofilename>: [NOSPEECH, REJECTION, SPOKETOOSOON, MAXSPEECHTIMEOUT]

• <audiofilename>: ERROR processing file

• Remember to run at confidence threshold of 0 . . .

“Missilences”

Misrecognitions

“Misrejections”

0 100Rejection Threshold

500

1000

ASR Engine B errors

Sum

MinimumTotal Error

0 100Rejection Threshold

500

1000

ASR Engine B errors

Comparing ASR accuracy

• Plot and compare

• Remember to compare optimal error rates of each (representing tuned accuracy)

Total errors: A vs B

ASR Engine A

0 100Rejection Threshold

500

1000

ASR Engine B

Comparison conclusions

• Optimal error rates are very similar on this data set

• Engine A is much more sensitive to rejection threshold changes

Natural Numbers

ASR Engine A

0 100

500

1000

ASR Engine B

Rejection Threshold

Note that optimal thresholds are

different!

Today we . . .

• Learned all the steps of doing an eval by actually doing them

• How to collect data

• Transcribing data

• Running a test

• Analyzing results

• Finished with code that runs(and some homework . . .)