Hollywood producers and directors have a strong affinity for creating films that use advanced technology. Among these movies’ commonality are systems that can distinguish voice and speech.
Take R2-D2 in “Star Wars,” for example. While the robot cannot speak in English, it can understand instructions and follow commands. The same is true for the ship computer in “Star Trek.” It can identify who is speaking and discern voice commands. Both films made viewers think that any computer can be easily programmed to make this distinction.
But speech recognition is far more complicated than that. In fact, the first speech technology named “Audrey,” only recognized digits spoken by one voice. Like any first version, it had several issues. Over time, though, Audrey paved the way for more modern speech recognition software with improved functionalities.
How Does Speech Recognition Work, Exactly?
With all the technology that surrounds us today, it can be tempting to overlook how speech recognition works. Many users believe they simply need to record their voices so their devices can recognize their speech and voice. But speech recognition is more than that.
To gain a better perspective, imagine how little children learn to speak. They start by getting familiar with the words they hear daily. Parents talk to their children, even if the latter cannot respond yet. At first, children process verbal cues, inflections, intonations, and pronunciations. All these get wired to their brains, and they start recognizing language patterns and connections.
That is how speech recognition works. Humans may have refined the process, but computers first need to figure out best practices to make sure they get it right. The best way to do that is to train computers, much like how parents teach their children. Imagine learning thousands of languages, dialects, and accents. All these must be taken into account, which is why it takes time to perfect speech recognition systems.
What Is the Speech Recognition Process Like?
Speech recognition systems begin by breaking down audio inputs into different individual sounds. Each sound is analyzed to identify the most plausible word that complements the language. Through natural language processing (NLP) and deep learning algorithms, computers can understand and derive meaning from human language by breaking down speech into little bits. These bits are then converted into digital data before they are analyzed. Once that is done, algorithms make assumptions based on programming and speech patterns and transcribe inputs into text.
The process may sound simple, but it is actually complicated as systems need to perform multiple operations at lightning speed.
What Are the Different Speech Recognition Methods?
Computers use different methods to recognize speech, which include:
Simple Pattern Matching
In this method, computers parse sentences or split them into separate words to figure out their structure. They identify numbers by comparing sounds with stored patterns in memory. This method is most commonly used in automated call centers where callers simply have to say numbers to get a response.
Pattern Analysis
More complex speech recognition systems do pattern analysis. They begin by recognizing spoken words by picking up phonemes that make up words. The word strings are then broken down into bits of phonemes. The idea is, when systems identify the phonemes, they can recognize the words.
Statistical Analysis
Speech is widely variable, and not everyone utters words the same way. Pronunciations can vary and change over time. It is necessary to come up with systems that can go beyond simple pattern recognition. That is where statistical analysis comes in. It develops a language model that uses probabilities to arrive at the best guesses. The only way to know it works is if the speaker accepts the translated text.
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As we have seen, perfecting speech recognition is a challenge. But to say we are not making headway would be entirely false.
In 2017, Google announced that its machine learning (ML) algorithms achieved a 95% word accuracy rate for English. That is pretty impressive, considering that the rate is the same threshold for human accuracy.
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