In the 1950s, people began to investigate the possibility of converting spoken language into written text, although these early systems were limited in their capabilities, often restricting themselves to recognising single digits or a small group of words. Since then, however, speech recognition systems have become more sophisticated and accurate, largely due to the advancement of machine learning algorithms, which enable computers to learn from vast amounts of data, recognise patterns and make predictions. For example, in the context of speech recognition, machine learning algorithms are used to analyse audio data and identify patterns that correspond to specific words or phrases, and the more data this system is exposed to, the better it can recognise speech, leading to greater accuracy and efficiency. Add to this the increasing availability of large data sets (Big Data), which provide the information necessary for machine learning algorithms to learn and improve. For example, the rise of social media and other online platforms has produced a wealth of text data that can be used to train speech recognition systems, and the proliferation of smartphones and other devices with built-in microphones has made it easier to collect audio data for analysis.
In the business world, speech recognition technology is being used to streamline customer service. Many companies now use automated systems that can understand and respond to customer queries, reducing the need for human operators and improving efficiency. In addition, transcription services that convert spoken language into written text are becoming increasingly popular, enabling businesses to transcribe meetings, interviews and other audio recordings quickly and accurately.