We are in 2021, and pretty much everyone knows how to use a computer. Statistically, that also means more people know that computers do not speak any spoken languages. Computers only understand 0’s and 1’s (binary).

That’s good and well, but if computers do not understand English or any other native language, how do they perform any language-related tasks?

For example, how does speech recognition work? How does a computer distinguish between the different speakers in a recording?

Is it even a computer that does the speech recognition, or is a human transcriber involved?

Let’s take a brief look at this technology to find the answers to these questions.

What is Speech Recognition?

Speech recognition combines multiple disciplines from computer science to identify speech patterns. Identifying speech patterns help computers differentiate between various speakers. It also assists in recording what each speaker is saying.

As is the case with most computer-related technologies, you will find references to speech recognition dating as far back as the mid 20th century.

But it wasn’t until the 2010’s that its practical applications became widely popular. But what are these applications?

Where is Speech Recognition used?

The most widely used application of speech recognition would be virtual assistants (e.g. Amazon’s Alexa). These virtual assistants are becoming so ubiquitous that you can access them even while you are driving!

Another industry that is using speech recognition technology extensively is the transcription industry.

Transcribing a one-way conversation is easier since there is only one speaker involved. As a result, computers or transcriptionists have to identify fewer speech patterns.

But to transcribe a full-blown conversation? That involves complexities that are difficult and time-consuming to solve, even for a computer.

Speech recognition technologies are used in the transcription industry to provide solutions for these complexities.

Similarly, many other industries and domains benefit from speech recognition technologies. It is helping solve complex business problems efficiently.

Why is Speech Recognition Difficult?

Speech recognition is challenging because human speech itself is not a simple affair! Many factors contribute to our speech patterns; some of these factors include profession, location, education, etc.

For example, here is a list of average speech rates for different activities:

  • Presentations: between 100 – 150 wpm for a comfortable pace
  • Conversational: between 120 – 150 wpm
  • Audiobooks: between 150 – 160 wpm, which is the upper range that people comfortably hear and vocalize words
  • Radio hosts and podcasters: between 150 – 160 wpm
  • Auctioneers: can speak at about 250 wpm
  • Commentators: between 250- 400 wpm

Looking at this list, it is clear that even the speed at which we speak changes based on what we are doing. And this is only one of the many factors that affect our speech patterns.

The resulting variations are difficult to keep track of for even a native speaker well-versed in that language. That should give you enough context as to why speech recognition is such a challenge to automate.

Through the use of cutting-edge voice AI technologies, Wavel has found a way to automate speech recognition. If you are looking for a time-efficient and accurate speech recognition solution for your transcriptions, click here to learn more.

How does Speech Recognition Work?

Early iterations of speech recognition software revolved around processing one spoken word at a time. This may feel like a sound strategy on the surface.

But how would the software know, for example, that you are making a mistake when you say bacon and legs instead of bacon and eggs? It could not because it was processing words without any context.

The reason behind this shortcoming was the limited computational resources made available to these precursor software suites.

Fast forward to today’s speech recognition software suites, like Alexa, Cortana, Siri, etc., processing power is no longer a bottleneck.

That is because these modern-day speech recognition software suites don’t rely on the end user’s device for processing. Instead, they live on the cloud with its massive resources and computational power.

As such, today’s speech recognition technologies can process phrases, sentences, and even entire paragraphs at a time to deliver the most favorable solution within the context of the said phrase, sentence, or paragraph.

However, that does not mean that speech recognition technologies have stopped evolving. On the contrary, they will keep on improving as computers become faster.

As a result of this continuous evolution, speech recognition technologies will become more accurate and time-efficient in the future.


At present, speech recognition technologies are assisting people in all walks of their lives. Whether you want to transcribe an interview or identify a new song your friends are playing without asking them, speech recognition got your back!

But despite the tremendous progress that speech recognition has witnessed in the past decade or two, there is still room for improvement. As newer and faster hardware enters the market, speech recognition is expected to work faster.

If you would like to reach out to us, you can do so by mailing us at reachout@wavel.co.

Sneha Mukherjee

Content and Copywriter at Wavel AI

I fuse my passion for technology with storytelling, breathing life into our innovative solutions through words. My mission transcends features, focusing on crafting engaging narratives that connect users and render AI accessible to all.