The Ultimate Guide to Understanding
Speech Recognition

Introduction to Speech Recognition

Have you ever talked to a computer? And no, shouting at it when the internet connection goes down doesn’t count. We mean talking to a computer where it actually recognizes what you say and then reacts accordingly.

If you have, whether on your smart phone or on a customer service line or in front of your laptop, then you have used speech recognition technology.

There are over 7 billion people in the world today speaking over 6,500 different languages. And those 7 billion people can understand one another as long as they speak one of the same languages between them.

Today, computers can recognize speech, too. Some years ago, this would have sounded like a sci fi movie, but thanks to one particular breakthrough in artificial intelligence, speech recognition is now a reality. And with that technology, you can now perform basic computer tasks without even touching a mouse or keyboard. You just give commands to your computer verbally.

Speech Recognition Definition

What is the defintion of Speech Recognition?

Speech recognition is the process where a computer (or any other device) identifies spoken words. This is not only the recognition of natural language, but natural language in its verbal form. Speech recognition enables users to control digital devices through voice commands instead of using conventional tools such as a keyboard, buttons, mouse or keystrokes. Speech recognition allows you to provide input by simply talking.

For instance, when checking your bank account balance, you might say, “Check account balance.” Your bank’s VoiceXML application then responds with, “one hundred twenty-six thousand, three hundred and eighty-seven dollars and seventy-eight cents.”

Pretty cool, huh?

Note: speech recognition is easily confused with voice recognition, but they are two different concepts. Both use a human voice as input. However, they use it differently. While speech recognition is the process of converting the spoken word into digital data, voice recognition is the process of identifying the actual person who spoke. Voice recognition is done by examining the unique features of speech that differ from one person to the other.

Would you like to learn more about commonly used words in Speech Recognition Terminology? Visit our Speech Recognition Glossary to discover must know industry terms.

Speech Recognition Terminology

What are terminology of Speech Recognition?

Artificial Intelligence (AI): The study and programming of computer systems that can perform tasks normally associated with human intelligence (including perception and discovery, decision-making, and speech recognition).

Phoneme: The smallest unit of speech. Phonemes help distinguish one word from another.

Algorithms: An equation or set of rules that a computer follows (coupled with AI decision-making capabilities).

Artificial Neural Networks: These are considered the “foundation of AI,” and refer to the pieces of the processing system that computers use to perform human-like, intelligent problem-solving.

Natural Language Processing (NLP): This is the area of AI that revolves around the study of machines interacting with humans through “natural” (i.e., human) language.

Machine Learning: This is where artificial intelligence equips a machine to learn and improve its processes automatically without human intervention.

Interactive Voice Response (IVR): This is the technical name for the interactive telephone systems that can respond to voice and dial pad commands.

User Interface (UI): This is where the program “comes to the surface” where it can interact with a user. UI refers to all the means a system uses to interact with humans, including the desktop, display screens, keyboards, and mouse.

Directed Dialogue: In contrast with a user speaking freely to the user interface, this is where specific, pre-determined phrases need to be utilized for the system to recognize the command and act on it. This is especially common in IVR systems on customer service telephone lines.

Conversational Interface: The conversational interface is where the system intakes natural language, which in the case of speech recognition will always be spoken.

Speech Engine: This is the software that takes spoken input, compares it to available vocabulary and assigns meaning to it.

Deep Learning: This is a subset of machine learning, which is a subset of AI. It’s considered “deep” because it has the ability to machine-learn from unstructured, unlabeled data on a partially or completely unsupervised basis.

Speech Recognition News

Latest developments in Speech Recognition News

News

The field of Speech Recognition is continually growing with new technology advancements, software improvements, and products. Staying up to date with the latest Speech Recognition news is important to stay on top of this rapidly growing industry. We cover the latest in artificial intelligence news, chatbot news, computer vision news, machine learning news, natural language processing news, speech recognition news and robotics news.

Discover the Latest Speech Recognition News

Speech Recognition Explained

What is Speech Recognition?

When was the last time you called a big company for some type of customer service request? If it was any time in the last 10 years, chances are your call was not answered by a customer care agent. Instead, an automated voice recording helped you navigate through the menu by instructing you to press certain buttons. Today, thanks to speech recognition, many companies are even doing away with pressing buttons altogether to clod through the menu. Instead, you’re directed to say certain words to navigate through the caller options.

Speech recognition uses natural language to trigger an action on the part of the machine. Our voices enable our digital devices to respond to commands that are increasingly delivered in idiomatic, user-specific and unique strings of speech. Voice commands are doing away with other, more “tired” methods of input like clicking, texting, and typing.

Interestingly, the trend in communication between users has veered away from phone calls and is now almost entirely text and message based. But we’re OK with talking to our computers—so OK with it, in fact, that speech recognition has expanded to include a majority of the technology that we use in our daily lives. Still want to text your mom back instead of calling her? Today, you can dictate your text message to your phone while driving, or tell your speaker system to “put on that new Justin Bieber song.”

Artificial intelligence and its many subsequent technologies have greatly evolved over the past few years and are poised to thrust more change upon us soon. Let’s look back at how it all began.

History of Speech Recognition

What is the history of Speech Recognition?

Speech recognition dates back to the mid-20th century. Its development through the decades can be compared to the speech development of a child, advancing from baby-talk levels of single syllables to creating vocabularies and then replying to questions instantly—just like Siri, for example, does today.

Some of these might surprise you, but let’s look at past developments that enabled people control their devices using their voice as early as the 1950s.

1950s -1960s

The first speech recognition system was built in 1952 and could only understand digits. It was known as Audrey, built by Bell Laboratories. The digits furthermore had to come from a single voice for the system to understand.

Ten years later, IBM built the “shoebox” machine, which could recognize sixteen English words. Labs around the world developed similar systems dedicated to speech recognition in this same frame of time. Some expanded speech recognition to nine consonants and four vowels instead of particular words.

1970s

It can be said that speech recognition took off in the 1970s. Interests and funding from the United States government enabled major strides in speech recognition in the 1970s. Perhaps most notably, the government funded the DoD’s DARPA Speech Understanding Research (SUR) program that was conducted between 1971 and 1976. Harpy, a system that could understand 1011 words, was developed in this time.

1980s

In the 1980s, more advanced systems that could recognize several thousand words were developed thanks to a statistical method known as the Hidden Markov method. We’ll talk more about this method over the course of this guide.

1990s

In the 1990s, scientists started to move towards a new linguistic approach when developing speech recognition systems. The notion that speech recognition had to be acoustically based was abandoned. Speech recognition systems were now programmed with the grammar rules of each respective language.

Speech Recognition Categories

What are the categories of Speech Recognition?

Speech recognition can be categorized using the following parameters:

Dependence on speaker
Recognition style

Dependence on speaker

Here, speech recognition systems are classified depending on whether or not they depend on the speaker. This means that systems can be trained by and to a single person’s voice, or to a general vocabulary regardless of the delivering voice.

Speaker-dependent
Speaker-independent

Speaker-dependent

Speaker-dependent speech recognition systems require the unique biometric characteristics of a single person’s voice in order to process the speech. These systems are “trained” by the person who will ultimately be using them. New users must first “train” the software themselves by speaking to it so it can analyze the way they talk. Speaker-dependent systems achieve higher accuracy of speech recognition than speaker-independent systems. However, they can only respond accurately to the person or people who trained them.

Speaker-independent

Speaker-independent systems do need training by the user. They can recognize speech from anybody according to a general vocabulary. Speaker-independent software is the only real option for businesses, for example—it’s not realistic to require each customer to train the customer service phone answer system to their voice. Speaker-independent systems are less accurate, but high accuracy limits can still be attained within processing limits, especially with recent developments in AI and speech recognition technology.

Recognition style

Speech recognition systems can also be classified depending on the type(s) of utterances (or natural language verbalizations) they can recognize.

Isolated
Connected
Continuous

Isolated

This type of system only understands separately-spoken words. The speaker must, therefore, pause between each word or command. These systems are generally set up to identify words of 0.96 seconds or less. And because these are the easiest to train and program, isolated speech recognition systems are the most common today.

Connected

Connected speech recognition system allow multiple words (or separate utterances) to be “run-together,” but with a minimal pause between them. These systems are set up to identify phrases of 1.92 seconds or less.

Continuous

Continuous recognition systems recognize the natural, conversational speech that we use in our daily lives.

How Does Speech Recognition work?

How Speech Recognition works?

Understanding speech is, of course, the first component of speech recognition. The first step in speech recognition is converting that spoken words into an electronic signal that can be categorized and processed into action. Initially, converting sound into that signal is done with a microphone. It is then converted into digital data that can be understood by the computer using an analog-to-digital converter (ADC).

To digitize the sound, the system depends on the Hidden Markov Model (HMM). The HMM approach assumes that when a speech signal is viewed on a very short timescale (say, five milliseconds), it can be approximated as a stationary process—or a process whose statistical properties do not change over time.

Precise measurements of the speech signal are taken at frequent intervals. The digitized sound is then filtered to remove any unwanted noise while categorizing the sound into different frequency bands. The sound is also “normalized,” or kept at a constant volume level. And the speed of speaking is not the same for every person, so the sound must also be modified to match the speed of the template in the computer system’s memory.

The speech signal is then divided into very small segments (following the HMM approach). Generally, speech signals are divided into 10-millisecond fragments. These fragments are then matched to known phonemes, or the smallest element of speech. The sound of phonemes differs with different people and even in different utterances by the same speaker, and so a special algorithm is used to establish the most likely word(s) that match the given pattern of phonemes.

We’re not as consistent as you’d like to think. Speech recognition software have been developed to account for that.

The whole process of speech recognition might seem computationally impractical. However, many modern speech recognition systems use neural networks to simplify the speech signal before HMM recognition. Neural networks use feature transformation and dimensionality reduction to simplify the speech signals. Voice activity detectors (VADs) are also applied to reduce an audio signal to the segments that contain speech. This reduces the time that could have been wasted in analyzing the unnecessary parts of the signal.

It still sounds complicated, doesn’t it? Is it even possible to perfect such a complicated process?

Not yet, but it is a work in progress and soon will be there.

So, maybe the next time Alexa plays the wrong song, bear in mind that this technology is extremely complicated—and impressively accurate, but not perfect. Just smile, forgive her and dance to the music she chose for you. And keep watch for what new products and services are introduced in coming years—we’ve barely scratched the surface of the changes that are underway.

Speech Recognition Companies

Discover innovative Speech Recognition startups and companies

It takes bold visionaries and risk-takers to build future technologies into realities. In the field of Speech Recognition, there are many companies across the globe working on this mission. Our mega list of artificial intelligence, chatbots, computer vision, machine learning, natural language processing, and speech recognition companies, covers the top companies and startups who are innovating in this space.

List of Speech Recognition Startup and Companies

Speech Recognition Key Components

Components of Speech Recognition

Speech recognition systems today consist of the following key components:

Speech capturing device

Speech capturing devices usually include a microphone and an ADC. A microphone is used to capture speech and convert the sound into electrical signals. The ADC converts the electrical signals into digital data that can be understood by the computer.

Digital signal processor

A digital signal module processes the raw speech signal, converting the frequency domain and retaining the necessary information only.

Preprocessed signal storage

A preprocessed speech signal is kept in the memory of a speech recognition system. This helps in carrying out further tasks on speech recognition based on a growing vocabulary base.

Reference speech patterns

Speech recognition systems contain predefined speech patterns already stored in the system’s memory that act as a reference for pattern matching.

Pattern matching algorithm

A special algorithm is used to determine the most likely word(s) by matching the speech patterns to the reference pattern in the speech recognition system’s memory.

Speech Recognition Applications

What are the applications of Speech Recognition?

Dictation

Dictation is probably the most common use of speech recognition today. Dictation solutions include speech-to-text software that allow the user to control their devices without typing. Dictation is mostly preferred in specific areas of business, such as legal and medical transcription.

Call centers and IVR systems

The telephone is still the most popular mode of communication between clients and organizations. And to provide high-quality level of more cost-effective self-service, organizations today turn to speech recognition. This enables customers to make their transactions without dealing with bulky touchtone menu mazes. It also enables the organizations to reduce the cost of hiring extra customer care agents.

Education

Speech recognition is also applied across many different applications in education. For example, dictation applications contribute to students’ reading, speaking and pronunciation abilities through providing feedback on their spoken input to fast-thinking programs.

Applications for the disabled

Speech recognition is very useful to disabled persons who cannot use other input methods such as clicking and typing on a keyboard. Speech commands allow them to control their devices through voice.

These are just a few applications of speech recognition. The list goes on and will only be getting longer.

Speech Recognition Tools

What are Speech Recognition tools?

Below are some of the toolkits that act as a foundation for building speech recognition systems. You can also read more on the website about companies who are pioneering future development for truly astounding speech recognition and AI technologies.