1. Automatic Speech Recognition

2. The Malevolent Hal

3. WE’RE Not Quite There Yet (and Lucky for Us)
But what is an error?

4. The Model
Speech is variable. An acoustic utterance will never match any model exactly.
Conclusion: Speech Recognition is a special case of Bayesian inference.

5. Goal Of a Probabilistic Noisy Channel Architecture
What is the most likely sequence of words W out of all word sequences in a language L given some acoustic input O?
Where O is a sequence of observations
0=o1, o2 , o3 , ..., ot
each oi is a floating point value representing ~10ms worth of energy of that slice of 0
And w=w1, w2 , w3 ,..., wn
each wi is a word in L

6. ASR as a Conditional Probability
We have to invoke you know whom

7. Bayes Rule
Let’s us transform To
In fact

8. New Terms Feature Extraction
Acoustic waveform is sampled into frames (10, 15, 20 ms)
Transformed into a vector of 39 features
Acoustic Model or Phone Recognition (likelihoods)
compute the likelihood of observed features given linguistic units (words, phones, triphones): p(O|W)
output is sequence of probabilities, one for each time frame
contains the likelihooods that each linguistic unit generated the acoustic feature vector

9. New Wine in Old Bottles Language Modeling (priors) Lexicon
bigrams/trigrams/quadrigrams of words in a lexicon
Lexicon
A list of words with a pronunciation for each word expressed as a phone sequence
Decoding (Viterbi)
Combines the acoustic model, language model and lexicon to produce the most probable sequence of words
Training
Filling in the HMM lattice using the Baum-Welch (forward-backward) algorithm
Observations: acoustic signals, information about the waveform at that point in time
Hidden states: phones/triphones

10. The architecture

11. It’s More Complex Phones can extend beyond 1 s That’s 100 frames
But they’re not acoustically identical

12. [ay k] ~.45s
Notice F2 rises and F1 falls and the difference between silence and release parts of [k]

13. Conclusion Phones are non-homogenous over time
Modeled with beginning, middle, end
six ([s ih k s]

14. The Formal Model
B = bi(ot) = p(ot|qi) The probability of a feature vector being generated by subphone state i