HMM part 1. Dr Philip Jackson

Transcription

1 Centre for Vision Speech & Signal Processing University of Surrey, Guildford GU2 7XH. HMM part 1 Dr Philip Jackson Probability fundamentals Markov models State topology diagrams Hidden Markov models - Likelihood calculation - Recognition & training F.1

2 Summary of Dynamic Time Warping The DTW approach allows efficient computation with limited flexibility in the alignment. It treats templates as deterministic with residual noise. Problems: 1. How much flexibility should we allow? 2. How should we penalise any warping? 3. How do we determine a fair distance metric? 4. How many templates should we register? 5. How do we select the best ones? Solution: Develop an inference framework to build templates based on the statistics of our data. F.2

3 Characteristics of the desired model 1. evolution of sequence should not be deterministic 2. observations are coloured depending on class 3. cannot directly observe class 4. stochastic sequence + stochastic observations Applications: automatic speech recognition optical character recognition protein and DNA sequencing speech synthesis noise-robust data transmission crytoanalysis machine translation image classification, etc. F.3

4 Probability fundamentals Normalisation Independent events Dependent events Bayes theorem Marginalisation F.4

5 Normalisation Discrete: probability of all possibilities sums to one: all X P (X) = 1 (1) Continuous: integral over the entire probabilty density function (pdf) comes to one: p(x) dx = 1 (2) Joint probability The joint probability that two independent events occur is the product of their individual probabilities: P (A, B) = P (A) P (B) (3) F.5

6 Conditional probability If two events are dependent, we need to determine their conditional probabilities. The joint probability is now P (A, B) = P (A) P (B A) (4) where P (B A) is the probability of event B given that A occurred; conversely, taking the events the other way P (A, B) = P (A B) P (B) (5) P (A, B) A Ā B B These expressions can be rearranged to yield the conditional probabilities. Also, we can combine them to obtain the theorem proposed by Rev. Thomas Bayes (C.18th). F.6

7 Bayes theorem Equating the RHS of eqs. 4 and 5 gives P (B A) = P (A B) P (B) P (A) (6) For example, in a word recognition application we have P (w O) = which can be interpreted as p(o w) P (w) p(o) (7) posterior = likelihood prior evidence (8) posterior probability gives basis for Bayesian inference, likelihood describes how likely are data for given class, prior incorporates other knowledge (e.g., language model), evidence normalises and is often discarded (as it is the same for all classes). F.7

8 Marginalisation Discrete: probability of event B, which depends on A, is the sum over A of all joint probabilities: P (B) = all A P (A, B) = all A P (B A) P (A) (9) Continuous: similarly, the nuisance factor x can be eliminated from its joint pdf with y: p(y) = p(x, y) dx = p(y x)p(x) dx (10) F.8

9 Introduction to Markov models We can model stochastic sequences using a Markov chain, e.g., the state topology of an ergodic Markov model: For 1st-order Markov chains, probability of state occupation depends only on the previous step (Rabiner, 1989): P (x t = j x t 1 = i, x t 2 = h,...) P (x t = j x t 1 = i) (11) So, if we assume the RHS of eq. 11 is independent of time, we can write the state-transition probabilities with the properties a ij = P (x t = j x t 1 = i), 1 i, j N (12) a ij 0 and N j=1 a ij = 1 i, j 1..N F.9

10 State duration characteristics As a consequence of the first-order Markov model, the probability of occupying a state for a given duration, τ, decays exponentially: p(x x 1 = i, M) = (a ii ) τ 1 (1 a ii ) (13) probability a duration F.10

11 Weather prediction example Let us represent the state of the weather by a 1st-order, ergodic Markov model, M: state 1: state 2: state 3: raining cloudy sunny with state-transition probabilities expressed in matrix form: A = { a ij } = (14) F.11

12 Weather predictor probability calculation Given today s weather what is the probability of directly observing the sequence of weather states rain-sun-sun with model M? A = rain cloud sun rain cloud sun P (X M) = P (X = {1, 3, 3} M) = P (x 1 = rain today) P (x 2 = sun x 1 = rain) P (x 3 = sun x 2 = sun) = a 1 a 13 a 33 = = F.12

13 Start and end of a state sequence Null states deal with the start and end of sequences, as in the state topology of this left-right Markov model: a 11 a 22 a 33 π 1 a 12 a 23 η Entry probabilities at t=1 for each state i are defined π i = P (x 1 = i) 1 i N (15) with the properties π i 0, and N i=1 π i = 1 for i 1..N Exit probabilities at t=t are similarly defined η i = P (x T = i) 1 i N (16) with properties η i 0, and η i + N j=1 a ij = 1 for i 1..N F.13

14 Parameters of the Markov Model, M State transition probabilities, A = {π j, a ij, η i } = {P (x t = j x t 1 = i)} where N is the number of states for 1 i, j N a 11 a 22 a 33 a 44 π 1 a 12 a 23 a η x x1 x2 x3 x4 x5 6 producing a sequence X = { 1, 1, 2, 3, 3, 4 } F.14

15 Example: probability of MM state sequence Consider the state topology state transition probabilities A = The probability of state sequence X = {1, 2, 2} is P (X M) = π 1 a 12 a 22 η 2 = = F.15

16 Summary of Markov models State topology diagram: Entry probabilities π = {π i } = [ ] and exit probabilities η = {η i } = [ ] T are combined with state transition probabilities in complete A matrix: A = Probability of a given state sequence X: P (X M) = π x1 T a xt 1 x t t=2 η xt (17) F.16

17 Introduction to hidden Markov models Hidden Markov Models (HMMs) use a Markov chain to model stochastic state sequences which emit stochastic observations, e.g., the state topology of an ergodic HMM: b b 3 2 b2 Probability of state i generating discrete observation o t, which has a value from a finite set k 1..K, is b i (o t ) = P (o t = k x t = i) (18) Probability distribution of a continuous observation o t, which takes a value from an infinite set, is b i (o t ) = p(o t x t = i) (19) We begin by considering only discrete observations. F.17

18 Observations in discretised feature space c 2 k =1 k =2 k =3 c 1 Discrete output probability histogram P(o) K k F.18

19 Parameters of a discrete HMM, λ State transition probabilities, A = {π j, a ij, η i } = {P (x t = j x t 1 = i)} where N is the number of states for 1 i, j N Discrete output probabilities, B = {b i (k)} = {P (o t = k x t = i)} where K is the number of observation types for 1 i N 1 k K a 11 a 22 a 33 a 44 π 1 a 12 a 23 a b 1(o 1) b 1(o 2) b 2(o 3) b 3(o 4) b 3(o 5) η 4 b 4(o 6) generating a state sequence X = { 1, 1, 2, 3, 3, 4 } and observations O = {o 1, o 2,..., o 6 } o 1 o 2 o 3 o 4 o 5 o 6 F.19

20 Procedure for generating an observation sequence 1. For t = 1, choose state x t = i using entry probability π i 2. Select o t = k according to b xt (k) 3. Transit according to a ij and η i, then respectively: (a) increment t, set x t = j and repeat from 2, or (b) terminate the sequence, t = T. a 11 a 22 a 33 a 44 π 1 a 12 a 23 a b 1(o 1) b 1(o 2) b 2(o 3) b 3(o 4) b 3(o 5) η 4 b 4(o 6) o 1 o 2 o 3 o 4 o 5 o 6 F.20

21 HMM probability calculation The joint likelihood of state and observation sequences is P (O, X λ) = P (X λ) P (O X, λ) (20) a 11 a 22 a 33 a 44 π 1 a 12 a 23 a b 1(o 1) b 1(o 2) b 2(o 3) b 3(o 4) b 3(o 5) η 4 b 4(o 6) o 1 o 2 o 3 o 4 o 5 o 6 The state sequence X = {1, 1, 2, 3, 3, 4} produces the set of observations O = {o 1, o 2,..., o 6 }: P (X λ) = π 1 a 11 a 12 a 23 a 33 a 34 η 4 P (O X, λ) = b 1 (o 1 ) b 1 (o 2 ) b 2 (o 3 ) b 3 (o 4 ) b 3 (o 5 ) b 4 (o 6 ) P (O, X λ) = π x1 b x1 (o 1 ) T t=2 a xt 1 x t b xt (o t ) η xt (21) F.21

22 Example: probability of HMM state sequence Consider state topology and state transition matrix: A = o1 o 2 o 3 Output probabilities: B = [ b1 b 2 ] = R G B [ ] Probability of observations with state sequence X = {1, 2, 2}: P (O, X λ) = P (X λ) P (O X, λ) = π 1 b 1 (o 1 ) a 12 b 2 (o 2 ) a 22 b 2 (o 3 ) η 2 = 1 = F.22

23 HMM Recognition & Training Three tasks within HMM framework 1. Compute likelihood of a set of observations with a given model, P (O λ) 2. Decode a test sequence by calculating the most likely path, X 3. Optimise pattern templates by training parameters in the models, Λ = {λ} State Observations F.23

24 HMM part 1 summary Probability fundamentals Normalisation and marginalisation Joint and conditional probabilities Bayes theorem Markov models sequence of directly observable states Hidden Markov models (HMMs) hidden state sequence generation of observations F.24