Communication plays an essential role in our lives. Humans started with signs, symbols, and then made progress to a stage, where they began communicating with languages. Later computing and communication technologies came. Machines began communicating with humans and in some cases, with themselves also. The communication created the world of the internet, or as we technically know the Internet of Things(IoT). Here is the evolution of speech recognition technology that involves machine learning.

The internet gave rise to new ways of using data. Using this, we can communicate directly or indirectly with machines by training them, which is known as Machine Learning. Before this, we have to access a computer to communicate with machines.

Research and development are beginning to eliminate some of the use of computers to a great extent. We know this technology as Automatic Speech Recognition. Based on Natural Language Processing (NLP), it allows us to interact with machines using our natural language in which we speak.

The initial research in the field of Speech Recognition has been successful. Since then, speech scientists and engineers aim to optimize the speech recognition engines correctly. The ultimate goal is to optimize the machines interaction according to the situations so that error rates can be reduced and efficiency can be increased.

Some organizations have already started the development of fine-tuning speech recognition technologies. For more than a decade, Virginia based GoVivace Inc. has continually specialized in the design and development of speech recognition technologies and solutions.

Automatic Speech Recognition(ASR) technology is a combination of two different branches Computer Science and Linguistics. Computer Science to design algorithms and to program and Linguistics to create a dictionary of words, sentences, and phrases.

The first stage of development starts with speech transcriptions, where the audio is converted into text, i.e., speech to text conversion. After this, the system removes unwanted signals or noise by filtering. We have different voice speeds while saying a word or sentence, so the general model of speech recognition is designed to account for those rate changes.

Later the signals are further divided to identify phonemes. Phonemes are the letters that have the same level of airflow, like b and p. After this, the program tries to match the exact word by making a comparison with words and sentences that are stored in the linguistics dictionary. Then, the speech recognition algorithm uses statistical and mathematical modeling to determine the exact word.

One type of system is accomplished with learning mode and other as a human dependent system. With developments in Artificial Intelligence(AI) and Big Data, speech recognition technology achieved the next level. A specific neural architecture called long short term memory bought a significant improvement in this field. Globally, organizations are leveraging the power of speech at their premises at different levels for a wide variety of tasks.

Speech to text software includes timestamps and confidence score for each word. Many countries do not have their language embedded keyboards, and a majority of people do not have an idea of using a specific language keyboard, though they are verbally good at it. In such cases, speech transcriptionshelp them to convert speech into text in any language.

The other use of this technology is in real-time. Tech done in real-time is known as Computer Assisted Real-Time translation. It is basically a speech to text system which operates on a real-time basis. Organizations all over the world perform meetings and conferences.

For maximum participation by global audiences, they leverage the power of live captioning systems. The real-timecaptioning system converts the speech to text and displays it on the output screen. It translates the speech in one language to the text of other languages and also helps in making notes of a presentation or a speech. These systems convert speech to text that is also understood by hearing-impaired people.

Apart from speech to text, the technology spreads its branch into the biometric system, which created voice biometrics for authentication of users. Voice biometric systems analyze the voice of the speaker, which depends on factors like modulation, pronunciations, and other elements.

In these systems, the sample voice of the speaker is analyzed and stored as a template. Whenever the user speaks the phrase or sentence, the voice biometrics system compares them with the stored template and provides authentication. However, these systems are facing a lot of challenges. Our voice is always affected by physical factors or emotional state.

The recent developments in biometric voice systems operate by matching the phrase with the sample. After this, it analyzes the voice patterns by taking psychological and behavioral voice signal into consideration. Also, the developments in voice biometrics technology are going to help enterprises where data security is a significant concern.

Analytics play an essential role in the development of speech recognition technology. Big data analysis created a need for storing voice data. Call centers started using the recorded calls for training their employees. Since customer satisfaction is now the primary focus of organizations around the globe. Now, organizations want to track and analyze the conversation between executives and customers.

With Call Analytics applications, organizations can monitor and measure the performance and analytics of call. This call analytical solution enhances the performance of services provided by call centers. Through this, one can classify their customers and can serve them better by giving faster and favorable responses.

Research in speech recognition technology has a long way to go. Until now, the program can act on instructions only. Human communication feel does not exist entirely with machines. Researchers are trying to inculcate the human responsiveness into machines. They have a long way to go in the innovation of speech recognition technology.

The primary feature of research concentrates on how to make speech recognition technology more accurate. For human language understanding, we need more accuracy. For example, a person raised a question, how do I change camera light settings? This question technically means that the individual wants to adjust the camera flash. So significant concentration is on understanding the free form language of humans before answering specific questions.

So overall, machine learning with speech recognition technology has already made its way into the organizations globally and started providing effective and efficient results. Very soon we might be seeing a day where the automated stenographer would get promoted and start taking an active part in organizing the meetings and presentations.

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Evolution of Speech Recognition Technology - ReadWrite