The **Viterbi algorithm** is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the **Viterbi path**—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM).

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 now also commonly used in speech recognition, speech synthesis, diarization,^{[1]} keyword spotting, computational linguistics, and bioinformatics. For example, in speech-to-text (speech recognition), the acoustic signal is treated as the observed sequence of events, and a string of text is considered to be the "hidden cause" of the acoustic signal. The Viterbi algorithm finds the most likely string of text given the acoustic signal.

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.^{[2]} 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.^{[3]} 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.^{[3]}
For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation (parse) of a string, which is commonly called the "Viterbi parse".^{[4]}^{[5]}^{[6]} Another application is in target tracking, where the track is computed that assigns a maximum likelihood to a sequence of observations.^{[7]}

A generalization of the Viterbi algorithm, termed the *max-sum algorithm* (or *max-product 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 an hidden Markov model (HMM), 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 (which is the generalization of the forward-backward algorithm).

With the algorithm called iterative Viterbi decoding one can find the subsequence of an observation that matches best (on average) to a given hidden Markov model. This algorithm is proposed by Qi Wang et al. to deal with turbo code.^{[8]} 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.^{[9]} For many applications of practical interest, under reasonable noise conditions, the lazy decoder (using Lazy Viterbi algorithm) is much faster than the original Viterbi decoder (using Viterbi algorithm). 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 (and never more) than the ordinary Viterbi algorithm for the same result. However, it is not so easy^{[clarification needed]} to parallelize in hardware.

This algorithm generates a path , which is a sequence of states that generate the observations with , where is the number of possible observations in the observation space .

Two 2-dimensional tables of size are constructed:

- Each element of stores the probability of the most likely path so far with that generates .
- Each element of stores of the most likely path so far

The table entries are filled by increasing order of :

- ,
- ,

with and as defined below. Note that does not need to appear in the latter expression, as it's non-negative and independent of and thus does not affect the argmax.

- Input

- The observation space ,
- the state space ,
- an array of initial probabilities such that stores the probability that ,
- a sequence of observations such that if the observation at time is ,
- transition matrix of size such that stores the transition probability of transiting from state to state ,
- emission matrix of size such that stores the probability of observing from state .

- Output

- The most likely hidden state sequence

functionVITERBIforeach statedoend forforeach observationdoforeach statedoend forend forfordoend forreturnend function

Restated in a succinct near-Python:

# probability == p. Tm: the transition matrix. Em: the emission matrix.functionviterbi(O, S, Π, Tm, Em): best_path trellis ← matrix(length(S), length(O)) # To hold p. of each state given each observation. pointers ← matrix(length(S), length(O)) # To hold backpointer to best prior state # Determine each hidden state's p. at time 0...fors in range(length(S)): trellis[s, 0] ← Π[s] * Em[s, O[0]] # ...and afterwards, tracking each state's most likely prior state, k.foro in range(1, length(O)):fors in range(length(S)):k← argmax(kin trellis[k, o-1] * Tm[k, s] * Em[s, o]) trellis[s, o] ← trellis[k, o-1] * Tm[k, s] * Em[s, o] pointers[s, o] ←kbest_path ← list()k← argmax(kin trellis[k, length(O)-1] ) # Find k of best final stateforo in range(length(O)-1, -1, -1): # Backtrack from last observation. best_path.insert(0, S[k]) # Insert previous state on most likely pathk← pointers[k, o] # Use backpointer to find best previous statereturnbest_path

- Explanation

Suppose we are given a hidden Markov model (HMM) with state space , initial probabilities of being in state and transition probabilities of transitioning from state to state . Say, we observe outputs . The most likely state sequence that produces the observations is given by the recurrence relations^{[10]}

Here is the probability of the most probable state sequence responsible for the first observations that have as its final state. The Viterbi path can be retrieved by saving back pointers that remember which state was used in the second equation. Let be the function that returns the value of used to compute if , or if . Then

Here we're using the standard definition of arg max.

The complexity of this implementation is . A better estimation exists if the maximum in the internal loop is instead found by iterating only over states that directly link to the current state (i.e. there is an edge from to ). Then using amortized analysis one can show that the complexity is , where is the number of edges in the graph.

Consider a village where all villagers are either healthy or have a fever, and only the village doctor can determine whether each has a fever. The doctor diagnoses fever by asking patients how they feel. The villagers may only answer that they feel normal, dizzy, or cold.

The doctor believes 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, there is a certain chance that a patient will tell the doctor "I feel normal", "I feel cold", or "I feel dizzy", depending on the patient's health condition.

The *observations* (normal, cold, dizzy) along with a *hidden* state (healthy, fever) form a hidden Markov model (HMM), and can be represented as follows in the Python programming language:

```
obs = ("normal", "cold", "dizzy")
states = ("Healthy", "Fever")
start_p = {"Healthy": 0.6, "Fever": 0.4}
trans_p = {
"Healthy": {"Healthy": 0.7, "Fever": 0.3},
"Fever": {"Healthy": 0.4, "Fever": 0.6},
}
emit_p = {
"Healthy": {"normal": 0.5, "cold": 0.4, "dizzy": 0.1},
"Fever": {"normal": 0.1, "cold": 0.3, "dizzy": 0.6},
}
```

In this piece of code, `start_p`

represents the doctor's belief about which state the HMM is in when the patient first visits (all the doctor knows is that the patient tends to be healthy). The particular probability distribution used here is not the equilibrium one, which is (given the transition probabilities) approximately `{'Healthy': 0.57, 'Fever': 0.43}`

. The `transition_p`

represents the change of the 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 `emit_p`

represents how likely each possible observation (normal, cold, or dizzy) is, given the underlying condition (healthy or fever). 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 patient visits three days in a row, and the doctor discovers that the patient feels normal on the first day, cold on the second day, and dizzy on the third day. The doctor has a question: what is the most likely sequence of health conditions of the patient that would explain these observations? This is answered by the Viterbi algorithm.

```
def viterbi(obs, states, start_p, trans_p, emit_p):
V = [{}]
for st in states:
V[0] [st] = {"prob": start_p[st] * emit_p[st] [obs[0]], "prev": None}
# Run Viterbi when t > 0
for t in range(1, len(obs)):
V.append({})
for st in states:
max_tr_prob = V[t - 1] [states[0]] ["prob"] * trans_p[states[0]] [st]
prev_st_selected = states[0]
for prev_st in states[1:]:
tr_prob = V[t - 1] [prev_st] ["prob"] * trans_p[prev_st] [st]
if tr_prob > max_tr_prob:
max_tr_prob = tr_prob
prev_st_selected = prev_st
max_prob = max_tr_prob * emit_p[st] [obs[t]]
V[t] [st] = {"prob": max_prob, "prev": prev_st_selected}
for line in dptable(V):
print(line)
opt = []
max_prob = 0.0
best_st = None
# Get most probable state and its backtrack
for st, data in V[-1].items():
if data["prob"] > max_prob:
max_prob = data["prob"]
best_st = st
opt.append(best_st)
previous = best_st
# Follow the backtrack till the first observation
for t in range(len(V) - 2, -1, -1):
opt.insert(0, V[t + 1] [previous] ["prev"])
previous = V[t + 1] [previous] ["prev"]
print ("The steps of states are " + " ".join(opt) + " with highest probability of %s" % max_prob)
def dptable(V):
# Print a table of steps from dictionary
yield " " * 5 + " ".join(("%3d" % i) for i in range(len(V)))
for state in V[0]:
yield "%.7s: " % state + " ".join("%.7s" % ("%lf" % v[state] ["prob"]) for v in V)
```

The function `viterbi`

takes the following arguments: `obs`

is the sequence of observations, e.g. `['normal', 'cold', 'dizzy']`

; `states`

is the set of hidden states; `start_p`

is the start probability; `trans_p`

are the transition probabilities; and `emit_p`

are the emission probabilities. For simplicity of code, we assume that the observation sequence `obs`

is non-empty and that `trans_p[i] [j]`

and `emit_p[i] [j]`

is defined for all states i,j.

In the running example, the forward/Viterbi algorithm is used as follows:

```
viterbi(obs,
states,
start_p,
trans_p,
emit_p)
```

The output of the script is

```
$ python viterbi_example.py
0 1 2
Healthy: 0.30000 0.08400 0.00588
Fever: 0.04000 0.02700 0.01512
The steps of states are Healthy Healthy Fever with highest probability of 0.01512
```

This reveals that the observations `['normal', 'cold', 'dizzy']`

were most likely generated by states `['Healthy', 'Healthy', 'Fever']`

. 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 (despite feeling cold that 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.

The **soft output Viterbi algorithm** (**SOVA**) 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 (with one branch being chosen to form the *Survivor Path*, and the other being discarded), the difference in the branch metrics (or *cost*) between the chosen and discarded branches indicate the *amount of error* in the choice.

This *cost* is accumulated over the entire sliding window (usually equals *at least* five constraint lengths), to indicate the *soft output* measure of reliability of the *hard bit decision* of the Viterbi algorithm.