Transcript Deterministic sequence recognition

So far:
• Historical introduction
• Mathematical background (e.g., pattern
classification, acoustics)
• Feature extraction for speech recognition (and some
neural processing)
• What sound units are typically defined
• Audio signal processing topics (pitch extraction,
perceptual audio coding, source separation, music
analysis)
• Now – back to pattern recognition, but include time
Deterministic Sequence
Recognition
Sequence recognition for ASR
• ASR = static pattern classification + sequence
recognition
• Deterministic sequence recognition: template
matching
• Templates are typically word-based;
don’t need phonetic sound units per se
• Still need to put together local distances into
something global (per word or utterance)
Front end analysis
• Basic approach the same for deterministic,
statistical:
– 25 ms windows (e.g., Hamming), 10 ms steps (a
frame)
– Some kind of cepstral analysis (e.g., MFCC or PLP)
– Cepstral vector at time n called xn
Speech sound categories
• Words, phones most common
• For template-based ASR, mostly words
• For template-based ASR, local distances based
on examples (reference frames) versus input
frames
From Frames to Sequence
• Easy if local matches are all correct (never
happens!)
• Local matches are unreliable
• Need measure of goodness of fit
• Need to integrate into global measure
• Need to consider all possible sequences
Templates: Isolated Word Example
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Matrix for comparison between frames
Word template = multiple feature vectors
Reference template = Xkref
Input template = X in
Need to find D( Xkref , X in )
Templates Matching Problem
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Time Normalization
Which references to use
Defining distances/costs
Endpoints for input templates
Time Normalization
• Linear Time Normalization
• Nonlinear Time Normalization – Dynamic Time
Warp (DTW)
Linear Time Normalization: Limitations
• Speech sounds stretch/compress differently
• Stop consonants versus vowels
• Need to normalize differently
Generalized Time Warping
• Permit many more variations
• Ideally, compare all possible time warpings
• Vintsyuk (1968): use dynamic programming
Dynamic programming
• Bellman optimality principle (1962): optimal
policy given optimal policies from sub
problems
• Best path through grid: if best path goes
through grid point, best path includes best
partial path to grid point
• Classic example: knapsack problem
Knapsack problem
• Stuffing a sack with items, different value
• Goal: maximize value in sack
• Key point 1: If max size is 10, and we know
values of solutions for max size of 9, we can
compute the final answer knowing the value
of adding items.
• Key point 2: Point 1 sounds recursive, but can
be made efficiently nonrecursive by building a
table
Basic DTW step w/ simple local constraints. Each (i,j) cell has local distance d and
cumulative distortion D. The eqn shows the basic computational step.
Dynamic Time Warp (DTW)
• Apply DP to ASR: Vintsyuk, Bridle, Sakoe
• Let D(i,j) = total distortion up to frame i in
input and frame j in reference
• Let d(i,j) = local distance between frame i in
input and frame j in reference
• Let p(i,j) = set of possible predecessors to
frame i in input and frame j in reference
• D(i,j) = d(i, j) + minp(i,j) D(p(i,j))
DTW steps
(1) Compute local distance d in 1st column(1st frame of
input) for each reference template.
Let D(0,j) = d(0,j) for each cell in each template
(2) For i=1 (2nd column), j=0, compute d(i,j) add to min of
all possible predecessor values of D to get local value
of D; repeat for each frame in each template.
(3) Repeat (2) for each column to the end of input
(4) For each template, find best D in last column of input
(5) Choose the word for the template with smallest D
DTW Complexity
• O(Nframesref . Nframesin . Ntemplates)
• Storage, though can just be
O(Nframesref . Ntemplates)
(store current column and previous column)
• Constant reduction: global constraints
• Constant reduction: local constraints
Typical global slope constraints for dynamic programming
Which reference templates?
• All examples?
• Prototypes?
• DTW-based global distances permit clustering
DTW-based K-means
• (1) Initialize (how many, where)
• (2) Assign examples to closest center (DTW
distance)
• (3) For each cluster, find template with
minimum value for maximum distance, call it
the center
• (4) Repeat (2) and (3) until some stopping
criterion is reached
• (5) Use center templates as references for ASR
Defining local distance
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Normalizing for scale
Cepstral weighting
Perceptual weighting, e.g., JND
Learning distances, e.g., with ANN, statistics
Endpoint detection: big problem!
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Sounds easy
Hard in practice (noise, reverb, gain issues)
Simple systems use energy, time thresholds
More complex ones also use spectrum
Can be tuned
Not robust
Connected Word ASR by DTW
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Time normalization
Recognition
Segmentation
Can’t have templates for all utterances
DP to the rescue
DP for Connected Word ASR by DTW
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Vintsyuk, Bridle, Sakoe
Sakoe: 2-level algorithm
Vintsyuk, Bridle: one stage
Ney explanation
Ney, H., “The use of a one-stage dynamic programming
algorithm for connected word recognition,” IEEE Trans.
Acoust. Speech Signal Process. 32: 263-271, 1984
Connected Algorithm
• In principle: one big distortion matrix
(for 20,000 words, 50 frames/word, 1000 frame
input [10 seconds] would be 109 cells!)
• Also required, backtracking matrix (since word
segmentation not known)
• Get best distortion
• Backtrack to get words
• Fundamental principle: find best segmentation
and classification as part of the same process, not
as sequential steps
DTW path for connected words
DTW for connected words
• In principle, backtracking matrix points back
to best previous cell
• Mostly just need backtrack to end of previous
word
• Simplifications possible
Storage efficiency
• Distortion matrix -> 2 columns
• Backtracking matrix -> 2 rows
• “From template” points to template with
lowest cost ending here
• “From frame” points to end frame of previous
word
More on connected templates
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“Within word” local constraints
“Between word” local constraints
Grammars
Transition costs
Knowledge-based segmentation
• DTW combines segmentation, time norm,
recognition; all segmentations considered
• Same feature vectors used everywhere
• Could segment separately, using acousticphonetic features cleverly
• Example: FEATURE, Ron Cole (1983)
Limitations of DTW approach
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No structure from subword units
Average or exemplar values only
Cross-word pronunciation effects not handled
Limited flexibility for distance/distortion
Limited mathematical basis
-> Statistics!
Epilog: “episodic” ASR
• Having examples can get interesting again
when there are many of them
• Potentially an augmentation of stat methods
• Recent experiments show decent results
• Somewhat different properties -> combination
The rest of the course
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Statistical ASR
Speech synthesis
Speaker recognition
Speaker diarization
Oral presentations on your projects
Written report on your project
Class project timing
• Week of April 30: no class Monday, double class
Wednesday May 2 (is that what people want?)
• 8 oral presentations by individuals, 12 minutes each
+ 3 minutes for questions
• 2 oral presentations by pairs – 17 minutes each + 3
minutes for questions
• 3:10 PM to 6 PM with a 10 minute mid-session break
• Written report due Wednesday May 9, no late
submissions (email attachment is fine)