What is speech recognition?

Speech recognition — more formally known as automatic speech recognition (ASR) — is the process used by a computer to understand and translate human speech into text in written form.

The key point about speech recognition is that it’s designed to work regardless of who’s speaking. In that respect, it differs from a voice recognition system, and we’ll explore the differences between these two technologies in more detail later on.

How does speech recognition work?

What is the speech recognition process computers use to transcribe the spoken word? The truth is that getting speech recognition right is complex. A speech recognition tool has to deal with wide variations in accents and vocal patterns and filter out background noise as much as possible.

Here’s a quick breakdown of how speech recognition works:

Technical basics: The traditional method

When a user speaks into a microphone, the computer processes the voice input, converting it into a digital signal, then compares it to generic voice patterns stored in the system.

It does this by breaking down the signal into its constituent phonemes (individual units of sound) using acoustic modeling. Then, it uses language modeling technology to determine the words used according to what it considers to be the most likely results.

This process is generally known as the “traditional hybrid” approach. The reason for this is that it combines three elements:

• A lexicon model that describes how words are spoken as a collection of phonemes, using lists custom-designed for each language

• An acoustic model that models and predicts which sounds are being spoken

• A language model that predicts which words are being spoken depending on statistical analysis of the probability of each word sequence appearing in the language

The increasing role of AI

The trouble with the traditional hybrid approach was that it had hit an accuracy ceiling. In other words, speech recognition technology that used a hybrid approach had become as accurate as it could ever be — and it was still far from meeting the ultimate goal of near-human accuracy.

Luckily, AI has changed all that. As AI technology has improved, it’s enabled the development of much more sophisticated speech recognition software. 

This uses end-to-end deep learning architectures, such as CTC, LAS, and RNNT, to train systems to produce much more accurate results without traditional hybrid methods.

The cloud as a key facilitator

The rise of the cloud as a resource is also vital to the popularity of these more accurate systems. The most significant impact it’s had on speech recognition has been on how it’s deployed in practical terms.

The proliferation of the cloud in business solutions has opened the door to a broad range of applications for speech recognition technology. 

Every department, from customer service to HR, can benefit from the cost-efficiency, flexibility, and scalability of implementing speech technology software on the cloud.

Key features of an effective speech recognition system

What is a speech recognition system? What essential features should an effective one have? Let’s take a look.

As we’ve already established, the use of AI and machine learning algorithms in speech recognition software has raised the game. Now, you can expect a top-end speech recognition system to be fully customizable according to each company’s business needs.

It should be able to:

• Recognize the nuances of speech patterns in multiple languages

• Use speaker labeling to identify individuals by the sound of their voice

• Implement profanity filtering to recognize and omit unwanted vocabulary from transcripts

• Recognize different brand names or terms and contextualize them

Speech recognition algorithms

So, what kind of algorithms does speech recognition software use? Here’s a brief rundown:

• Natural Language Processing (NLP): This field of AI specializes in human-to-machine interaction via speech or text. Computers use computational linguistics, machine learning, and statistical modeling to understand and generate appropriate speech.

• Hidden Markov Models (HMMs): Computers use HMMs to model speech as a sequence of discrete states; each state is a single phoneme or group of phonemes.

• N-grams: These models break down text into a list of successive items in a text document. This could be words, punctuation marks, numbers, or other symbols.

• Deep Neural Networks (DNNs): DNNs understand speech by modeling it as a hierarchy of data points. This makes them ideal for techniques that include acoustic and language modeling.

• Speaker Diarization (SD): SD is specifically a process used for voice recognition. It allows the computer to break down an audio stream into different segments, with one for each speaker.

What are speech recognition devices?

Speech recognition devices have become much more popular as the technology behind them has achieved greater accuracy. Here are some examples of devices you‘re probably familiar with that rely on speech recognition software to operate:

• Smart speakers: According to Technavio, the size of the U.S. smart home speaker market is expected to grow by an average of 20.6% between 2023 and 2028. That’s not surprising. Devices like Amazon’s Echo Dot and Google Nest Mini have revolutionized how many people interact with technology in the home.

• Voice assistants: Speech recognition is the foundation upon which applications like the Vonage Voice API are built. These systems are ideal for customer service teams because you can set up automated voice conversation flows, which helps boost productivity in service delivery.

• Transcription tools: Many transcription tools are available to meet all kinds of business needs. Some examples include Rev, which focuses on editing tools, and Alice, a good entry-level option thanks to its relatively affordable pricing system.

• Hands-free devices: Finally, we couldn’t have a list like this without including hands-free devices. Speech recognition technology allows you to use devices like your smartphone without touching them, which is great when you want to conduct business on the go.

How accurate is speech recognition technology?

As we mentioned in our speech vs. voice recognition comparison table, voice recognition tends to be more accurate than speech recognition.

This is because it’s specifically trained on a specific individual user’s voice template, whereas speech recognition software has to be able to deal with a wide variety of voices and accents.

Overall, you can expect an accuracy level of about 90%-95% with speech recognition software and about 98% with voice recognition tools.

Is it safe to use speech recognition?

Safety and security are top concerns when using any kind of new technology. Although developers prioritize it when creating speech recognition applications, it’s true to say that this kind of software has some vulnerabilities you should be aware of.

For instance, cybercriminals sometimes clone audio captured from a phone call or an online video. They can then create a fake voice sample and access accounts using voice response login. The risk of this happening to you is fairly low, but you should be aware of the possibility.

Also, one downside of smart speakers is that they’re essentially eavesdropping on you 100% of the time, which means they can store a lot of personal information about you.

Of course, none of this has to be a problem as long as you take a few basic precautions. Make sure your firmware and software remain up to date, for example, and change your user credentials from the factory defaults.

As in life, vigilance (but not paranoia) should be the watchword. Generally speaking, voice recognition systems are about as secure as any other piece of high-end technology.

Automate and streamline your communication processes with speech recognition 

Speech recognition software can help your business streamline your communications via automation, significantly boosting efficiency and productivity.

Explore the Vonage Automatic Speech Recognition solution today, and also check out how the wide range of Vonage Communications APIs can help you create superb customer experiences in every aspect of service delivery.