The noisy channel model is a framework that computers use to check spelling, answer questions, recognize speech, and perform machine translation. It aims to determine the correct word if you type its misspelled version or mispronounce it.

The noisy channel model can correct several typing mistakes, including missing letters (changing “leter” to “letter”), accidental letter additions (replacing “misstake” with “mistake”), swapped letters (changing “recieve” to “receive”), and replacing incorrect letters (replacing “fimite” with “finite”).

Other interesting terms…

Read More about the “Noisy Channel Model

The noisy channel model’s primary goal is to correct errors. Its technical formula is complicated, but you can learn more about how it works from this video:

Noisy Channel Model Applications

Apart from correcting misspellings, the noisy channel model lets computers answer questions, recognize speech, and perform machine translations. The following subsections discuss each of these applications in greater detail.

Using the Noisy Channel Model to Answer Questions

Computers designed to answer questions generally use two components—an information retrieval (IR) engine and an answer identifier module.

The IR engine retrieves a set of documents or sentences that may contain answers to a given question. The answer identifier module, meanwhile, identifies a substring of the document or sentence to determine possible answers. Each of these replies is assigned a score, depending on how likely they are to be the correct answer.

Let’s look at an example. To answer the question “Who is the leader of France?,” a computer may consider an answer like “Henri Hadjenberg, who is the leader of France’s Jewish community, endorsed confronting the specter of the Vichy past.” That is because the statement has the string “leader of France,” just like the question. That is incorrect, though, as the question may be referring to the country’s president. That’s why developers devised databases (think of them as dictionaries and thesauri built into IR engines) to create a list of possible answers that get rated later on.

The database translates the string “leader” into “president,” allowing the computer or your browser to give you this answer when you type “Who is the leader of France?” into the search field:

Using the Noisy Channel Model to Recognize Speech

The noisy channel model for speech recognition works the same way. Instead of equipping a computer with a dictionary and/or thesaurus, a sound database is connected. This repository contains word sounds that the machine translates into code to understand what speakers say.

Speakers need to pronounce words the same way they were recorded in the database. That’s why Siri or Alexa asks you to repeat commands sometimes. You need to pronounce the words in the manner the module understands before it can give you the answer to your question or follow your command.

Using the Noisy Channel Model for Machine Translation

In Google Translate and similar translators, the noisy channel model uses databases with the equivalents of a word in all the languages it can translate to, typically shown via a drop-down list.

To translate a French sentence into English, for example, the computer breaks the sentence into words first. So, to translate the statement “Emmanuel Macron est l’actuel président de la France.” into English, the application goes about the process word for word:

  • est = is
  • current = l’actuel
  • president = président
  • of = de la

Google Translate then applies conjugation rules to the statement, giving you this result:

Today’s spell checkers, browsers and other answer providers, speech recognition applications, and translators still have imperfections, but that may change for the better as their engines and modules are improved over time. For now, thanks to the noisy channel model, we get to enjoy advanced features for writing and seeking information.