1. Speech Recognition

2. What makes speech recognition hard?

3. Speech Recognition Task: Identify sequence of words uttered by speaker, given acoustic waveform. Uncertainty introduced by noise, speaker error, variation in pronunciation, homonyms, etc. Thus speech recognition is viewed as problem of probabilistic inference.

4. Example: “I’m firsty, um, can I haf somefing to dwink?” From Russell and Norvig, Artificial Intelligence

5. Speech Recognition System Architecture (from Buchsbaum & Giancarlo paper) Here, “lattice” means “Hidden Markov Model” Acoustic feature extraction Acoustic Features–>Phones model Phones–>Word pronounciation model Language model

6. Acoustic feature extraction From Russell and Norvig, Artificial Intelligence

8. Hidden Markov Models Markov model: Given state X t, what is probability of transitioning to next state X t+1 ? E.g., word bigram probabilities give P (word t+1 | word t ) Hidden Markov model: There are observable states (e.g., signal S) and “hidden” states (e.g., Words). HMM represents probabilities of hidden states given observable states.

9. Phone model P( phone | frame features) =  P(frame features| phone) P(phone) P(frame features| phone) often represented by Gaussian mixture model

10. From Russell and Norvig, Artificial Intelligence Acoustic Features–>Phones model

11. Word Pronunciation model Now we want P (words|phones 1:t ) =  P(phones 1:t | words) P(words) Represent P(phones 1:t | words) as an HMM Phones–>Word pronounciation model

12. Example of Phones–>Word pronounciation model From Russell and Norvig, Artificial Intelligence

13. Language model

14. To build a speech recognition system, need: Lots of data Acoustic signal processing tools Methods for learning various probability models Methods for “maximum likelihood” calculation (i.e., search or “decoding”): Suppose we have observations (features from acoustic signal) O= (o 1 o 2 o 3 … o n ). We want to find W* = (w 1 w 2 w 3 … w n ) such that

15. To build a speech recognition system, need: Lots of data Acoustic signal processing tools Methods for learning various probability models Methods for “maximum likelihood” calculation (i.e., search or “decoding”): Suppose we have observations (features from acoustic signal) O= (o 1 o 2 o 3 … o n ). We want to find W* = (w 1 w 2 w 3 … w n ) such that Language model Combine phone models, segmentation models, word pronunciation models Search or “decoding” method

16. To build a speech recognition system, need: Lots of data Acoustic signal processing tools Methods for learning various probability models Methods for “maximum likelihood” calculation (i.e., search or “decoding”): Suppose we have observations (features from acoustic signal) O= (o 1 o 2 o 3 … o n ). We want to find W* = (w 1 w 2 w 3 … w n ) such that Language model Combine phone models, segmentation models, word pronunciation models Search or “decoding” method

17. Emotion recognition in speech (by OES high-school students!) http://www.youtube.com/watch?v=NnbsGyViN3Y