Data Science Behind Speech Recognition Applications

Data Science is assisting the speech and talk applications by recognizing the voice message effectively given by the users and produce accurate text output in the response. This technique is widely used by most of the popular and tech giants like Google, Microsoft, Apple, etc., in their products to detect the input voice waves and then convert it into the text message to make the digital communication easier and faster than the traditional typing methods.

The most reliable technique used to make an exact and accurate Speech Recognition results is Deep Learning. It made Speech Recognition methods accurate and reliable enough to be applied to the outside environment in a controlled manner.

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In one of the traditional ways, the sound waves used to be directly fetched and processed under a simple Neural Network to produce the text output. But, the problem with this simple voice conversion technique is that the speech can’t always be entered in the same pitch and speed. And, as the speech and input voice vary in speed and modulation length, the system has to be powerful enough to detect the same and right words independent of the elaboration of the words.

Whether the input is given as "Hello" or in an elaborated way "Helloo", both the sound waves should be saved and detected as the same word "Hello". This aligning of audio files having variable lengths and convert it into fixed-length text requires extra processing.

The first step to converting a voice message into text is to create data bits of the voice waves. These data bits are then arranged in a large number of data sets and arrays by using the process called Sampling. In the process of Data Sampling, the Nyquist Theorem is also used for the analog and digital signals when there is a gap in the signals, to produce the continuous data bits for the input voice.

After processing of the data bits, the characters are then recognized by using the powerful Recurrent Neural Network. In this system, the audio chunks are organized in its memory in such a way that it enables the future predictions for the next alphabet by processing the audio bits. This is how the audio is processed in the series of data bits and produce the accurate texts for speech recognition and voice communication. The most common applications that use Speech Recognition techniques are Siri by Apple, Google Voice Search, Microsoft Cortana, etc.

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