The Evolution of Speech Analytics

Evolution of Speech Analytics

While the basic concept of speech recognition has been around for decades, some critical developments in the field have led to its advancement. The development of a computerized assistant, or AI, is one example. The invention of the Shoebox, a humanoid doll with the ability to understand 16 words of English, was a huge step forward. The Shoebox could then interpret a microphone turned sounds into electrical impulses. In 1971, IBM invented the Automatic Call Identification System (ACID), which allowed engineers to talk to machines and made speech recognition possible. The Harpy system was developed at Carnegie Mellon University and broke the ninety-five percent accuracy threshold, making it the first machine-readable speech-recognition system.

The first AI-driven AI-based speech recognition system was created in 1976. This system utilized the Viterbi algorithm and included an acoustic and language model and a hidden Markov model. This method used statistical models to calculate the probability of an unknown sound. The system’s ability to learn and process a massive amount of data paved the way for the rise of voice-activated menus.

The next breakthrough in speech recognition technology occurred in 2001. The IBM Shoebox recognized sixteen words within a few hundred milliseconds. It was much faster than earlier systems, which based their recognition on sound bits instead of words. The technology has improved dramatically since then and even recognizes accents and different languages. And it has become much more affordable to train and use than ever before. More people will benefit from speech-recognition software in their homes with this advancement.

In the early 1990s, speech recognition software was considered a near-impossible goal. However, after the terrorist attacks of September 11, the technology recognized tens of thousands of words. By 2000, this technology was capable of understanding over one thousand words per minute. While the accuracy of these systems was still very low, they had a much broader vocabulary and could recognize four vowels.

Speech recognition began in the 1980s when doctors would dictate their notes on cassette tapes. The resulting voice files were then transcribed and entered into patient records. After the attacks, the technology improved and became more affordable. Most commercial speech recognition systems have a more extensive vocabulary than the average human. These systems require large amounts of training but are becoming an indispensable tool for many businesses. It is used in healthcare and intelligence agencies.

As speech recognition has become more advanced, its vocabulary has expanded. Several applications include phonetics, voice dialing, and domotic appliances. Some of these systems are even capable of performing a range of tasks. Eventually, these systems will understand spoken commands in their natural language. Further developments are slated to make the technology even more accessible. Its history is full of innovation.