From last time …
ASR System Architecture
Grammar
Cepstrum
Speech
Signal
Signal
Processing
Recognized
Words
“zero”
“three”
“two”
Probabilities
“z” -0.81
“th” = 0.15
“t” = 0.03
Probability
Estimator
Decoder
Pronunciation
Lexicon
A Few Points about Human
Speech Recognition
(See Chapter 18 for much more on
this)
Human Speech Recognition
• Experiments dating from 1918 dealing with
noise, reduced BW (Fletcher)
• Statistics of CVC perception
• Comparisons between human and machine
speech recognition
• A few thoughts
The Ear
The Cochlea
Assessing Recognition Accuracy
• Intelligibility
• Articulation - Fletcher experiments
– CVC, VC, CV, syllables in carrier sentences
– Tests over different SNR, bands
– Example: “The first group is `mav’ (forced
choice between mav and nav)
– Used sharp lowpass and/or highpass filtered.
For equal energy, crossover is 450 Hz; for
equal articulation, 1550 Hz.
Results
• S = vc2
• Articulation Index (the original “AI”)
• Error independence between bands
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Articulatory band ~ 1 mm along basilar membrane
20 filters between 300 and 8000 Hz
A single zero error band -> no error!
Robustness to a range of problems
AI = ∑k 1/K (SNRk / 30)
where SNR saturates at 0 and 30
AI additivity
• s(a,b) = phone accuracy for band from a to b, a<b<c
• (1-s(a,c)) = (1-s(a,b))(1-s(b,c))
• log10(1-s(a,c)) = log10(1-s(a,b)) + log10(1-s(b,c))
• AI(s) = log10(1-s) / log10(1-smax)
• AI(s(a,c)) = AI(s(a,b)) + AI(s(b,c))
Jont Allen interpretation:
The Big Idea
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Humans don’t use frame-like spectral templates
Instead, partial recognition in bands
Combined for phonetic (syllabic?) recognition
Important for 3 reasons:
– Based on decades of listening experiments
– Based on a theoretical structure that matched the results
– Different from what ASR systems do
Questions about AI
• Based on phones - the right unit for fluent speech?
• Lost correlation between distant bands?
• Lippmann experiments, disjoint bands
– Signal above 8 kHz helps a lot in combination with
signal below 800 Hz
Human SR vs ASR:
Quantitative Comparisons
• Lippmann compilation (see book): typically
~factor of 10 in WER
• Hasn’t changed too much since his study
• Keep in mind this caveat: “human” scores are
ideal - under sustained real conditions people
don’t pay perfect attention (especially after lunch)
Human SR vs ASR:
Quantitative Comparisons (2)
System
10 dB SNR
16 dB SNR
“Quiet”
Baseline HMM
ASR
77.4%
42.2%
7.2%
ASR w/ noise
compensation
12.8%
10.0%
-
Human Listener
1.1%
1.0%
0.9%
Word error rates for 5000 word Wall Street Journal
read speech task using additive automotive noise
(old numbers – ASR would be a bit better now)
Human SR vs ASR:
Qualitative Comparisons
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Signal processing
Subword recognition
Temporal integration
Higher level information
Human SR vs ASR:
Signal Processing
• Many maps vs one
• Sampled across time-frequency vs sampled in time
• Some hearing-based signal processing already in
ASR
Human SR vs ASR:
Subword Recognition
• Knowing what is important (from the maps)
• Combining it optimally
Human SR vs ASR:
Temporal Integration
• Using or ignoring duration (e.g., VOT)
• Compensating for rapid speech
• Incorporating multiple time scales
Human SR vs ASR:
Higher levels
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Syntax
Semantics
Pragmatics
Getting the gist
Dialog to learn more
Human SR vs ASR:
Conclusions
• When we pay attention, human SR much better
than ASR
• Some aspects of human models going into ASR
• Probably much more to do, when we learn how to
do it right