Viterbi algorithm


The Viterbi algorithm is a dynamic programming algorithm that finds the most likely sequence of hidden events that would explain a sequence of observed events. The result of the algorithm is often called the Viterbi path. It is most commonly used with hidden Markov models. For example, if a doctor observes a patient's symptoms over several days, the Viterbi algorithm could determine the most probable sequence of underlying health conditions that caused those symptoms.
The algorithm has found universal application in decoding the convolutional codes used in both CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications, and 802.11 wireless LANs. It is also commonly used in speech recognition, speech synthesis, diarization, keyword spotting, computational linguistics, and bioinformatics. For instance, in speech-to-text, the acoustic signal is the observed sequence, and a string of text is the "hidden cause" of that signal. The Viterbi algorithm finds the most likely string of text given the acoustic signal.

History

The Viterbi algorithm is named after Andrew Viterbi, who proposed it in 1967 as a decoding algorithm for convolutional codes over noisy digital communication links. It has, however, a history of multiple invention, with at least seven independent discoveries, including those by Viterbi, Needleman and Wunsch, and Wagner and Fischer. It was introduced to natural language processing as a method of part-of-speech tagging as early as 1987.
Viterbi path and Viterbi algorithm have become standard terms for the application of dynamic programming algorithms to maximization problems involving probabilities.
For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation of a string, which is commonly called the "Viterbi parse". Another application is in target tracking, where the track is computed that assigns a maximum likelihood to a sequence of observations.

Algorithm

Given a hidden Markov model with a set of hidden states and a sequence of observations, the Viterbi algorithm finds the most likely sequence of states that could have produced those observations. At each time step, the algorithm solves the subproblem where only the observations up to are considered.
Two matrices of size are constructed:
  • contains the maximum probability of ending up at state at observation, out of all possible sequences of states leading up to it.
  • tracks the previous state that was used before in this maximum probability state sequence.
Let and be the initial and transition probabilities respectively, and let be the probability of observing at state. Then the values of are given by the recurrence relation
The formula for is identical for, except that is replaced with [Arg max|], and.
The Viterbi path can be found by selecting the maximum of at the final timestep, and following in reverse.

Pseudocode

function Viterbi is
input states: S hidden states
input init: initial probabilities of each state
input trans: S × S transition matrix
input emit: S × O emission matrix
input obs: sequence of T observations
prob ← T × S matrix of zeroes
prev ← empty T × S matrix
for 'each state s in states do
prob = init * emit * trans * emit then
prob ← new_prob
prev ← r
path ← empty array of length T
path ← the state s with maximum prob
for t = T - 2 to 0 inclusive do
path ← prevpath[t + 1
return path
end'
The [time complexity of the algorithm is. If it is known which state transitions have non-zero probability, an improved bound can be found by iterating over only those which link to in the inner loop. Then using amortized analysis one can show that the complexity is, where is the number of edges in the graph, i.e. the number of non-zero entries in the transition matrix.

Example

A doctor wishes to determine whether patients are healthy or have a fever. The only information the doctor can obtain is by asking patients how they feel. The patients may report that they either feel normal, dizzy, or cold.
It is believed that the health condition of the patients operates as a discrete Markov chain. There are two states, "healthy" and "fever", but the doctor cannot observe them directly; they are hidden from the doctor. On each day, the chance that a patient tells the doctor "I feel normal", "I feel cold", or "I feel dizzy", depends only on the patient's health condition on that day.
The observations along with the hidden states form a hidden Markov model. From past experience, the probabilities of this model have been estimated as:

init = 
trans = 
emit =

In this code, init represents the doctor's belief about how likely the patient is to be healthy initially. Note that the particular probability distribution used here is not the equilibrium one, which would be according to the transition probabilities. The transition probabilities trans represent the change of health condition in the underlying Markov chain. In this example, a patient who is healthy today has only a 30% chance of having a fever tomorrow. The emission probabilities emit represent how likely each possible observation is, given the underlying condition. A patient who is healthy has a 50% chance of feeling normal; one who has a fever has a 60% chance of feeling dizzy.
A particular patient visits three days in a row, and reports feeling normal on the first day, cold on the second day, and dizzy on the third day.
Firstly, the probabilities of being healthy or having a fever on the first day are calculated. The probability that a patient will be healthy on the first day and report feeling normal is. Similarly, the probability that a patient will have a fever on the first day and report feeling normal is.
The probabilities for each of the following days can be calculated from the previous day directly. For example, the highest chance of being healthy on the second day and reporting to be cold, following reporting being normal on the first day, is the maximum of and. This suggests it is more likely that the patient was healthy for both of those days, rather than having a fever and recovering.
The rest of the probabilities are summarised in the following table:
Day123
ObservationNormalColdDizzy
Healthy0.30.0840.00588
Fever0.040.0270.01512

From the table, it can be seen that the patient most likely had a fever on the third day. Furthermore, there exists a sequence of states ending on "fever", of which the probability of producing the given observations is 0.01512. This sequence is precisely, which can be found be tracing back which states were used when calculating the maxima. In other words, given the observed activities, the patient was most likely to have been healthy on the first day and also on the second day, and only to have contracted a fever on the third day.
The operation of Viterbi's algorithm can be visualized by means of a trellis diagram. The Viterbi path is essentially the shortest path through this trellis.

Extensions

A generalization of the Viterbi algorithm, termed the max-sum algorithm can be used to find the most likely assignment of all or some subset of latent variables in a large number of graphical models, e.g. Bayesian networks, Markov random fields and conditional random fields. The latent variables need, in general, to be connected in a way somewhat similar to a hidden Markov model, with a limited number of connections between variables and some type of linear structure among the variables. The general algorithm involves message passing and is substantially similar to the belief propagation algorithm.
With an algorithm called iterative Viterbi decoding, one can find the subsequence of an observation that matches best to a given hidden Markov model. This algorithm is proposed by Qi Wang et al. to deal with turbo code. Iterative Viterbi decoding works by iteratively invoking a modified Viterbi algorithm, reestimating the score for a filler until convergence.
An alternative algorithm, the Lazy Viterbi algorithm, has been proposed. For many applications of practical interest, under reasonable noise conditions, the lazy decoder is much faster than the original Viterbi decoder. While the original Viterbi algorithm calculates every node in the trellis of possible outcomes, the Lazy Viterbi algorithm maintains a prioritized list of nodes to evaluate in order, and the number of calculations required is typically fewer than the ordinary Viterbi algorithm for the same result. However, it is not so easy to parallelize in hardware.

Soft output Viterbi algorithm

The soft output Viterbi algorithm is a variant of the classical Viterbi algorithm.
SOVA differs from the classical Viterbi algorithm in that it uses a modified path metric which takes into account the a priori probabilities of the input symbols, and produces a soft output indicating the reliability of the decision.
The first step in the SOVA is the selection of the survivor path, passing through one unique node at each time instant, t. Since each node has 2 branches converging at it, the difference in the branch metrics between the chosen and discarded branches indicate the amount of error in the choice.
This cost is accumulated over the entire sliding window, to indicate the soft output measure of reliability of the hard bit decision of the Viterbi algorithm.

General references

Implementations

  • has an implementation as part of its support for stochastic processes
  • signal processing framework provides the C++ implementation for Forward error correction codes and channel equalization .
  • includes code for Viterbi decoding.