The fundamental motive of speech is communication. Humans have been trying to improve their communication skills from as early as the 1960s, and the latest method is Digital Speech Processing. Digital Speech refers to the audio files sent, received, or stored in the digital form in layman’s terms. It also includes the voice messages you sent over WhatsApp, Messenger, etc., or via calls.
The audio files signify continuously varying acoustic waveforms(analog), known as Speech Signals. These speech signals are then converted into electrical waveforms to transmit the message through the wires, converted into acoustic forms by a loudspeaker or a telephone headset. This was the most primitive way of Speech Processing.
What is Speech Processing?
Speech Processing is the study of speech signals and their processing methods. Speech or voice is generally processed digitally and therefore regarded as a particular case of Digital Signal Processing. Digital Signal Processing refers to processing real-world digital signals like audio, video, temperature, pressure, etc., and then mathematically manipulating them.
Early attempts at speech processing could identify specific phonetics elements such as vowels. But with time, even call routing became a piece of cake. But the most impactful aspect of speech recognition was the introduction of AI and Machine Learning algorithms. Acquisition, manipulation, storage, transfer, and output of speech signals are some of the most common aspects of Speech Processing.
What are the techniques of Speech Processing?
With advancing technology and modern algorithms, processing speech varies a lot. But there are four fundamental ways used for speech processing. They are:
Dynamic Time Warping
It is an algorithm to measure similar temporal sequences, varying in time. In layman terms, the Dynamic Time Warping algorithm calculates the distance between two time series with certain restrictions and rules to see if the two given sequences match or not. As you might have guessed, it is a speech recognition algorithm.
Hidden Markov Model
Hidden Markov Model or HMM is one of the most widely used or successful Speech Recognition algorithms. The Markov Model converts spoken utterances into stationary and non-stationary states and thus enhances its accuracy and speech recognition abilities.
Artificial Neural Networks
An Artificial Neural Network (ANN) is a collection of nodes called artificial neurons, representing biological neurons. A neural network learns and produces more accurate results after each interaction. It is one of the more modern speech processing techniques.
Phase-aware Processing
Use the estimations obtained from this algorithm to reduce noise reduction and temporal soothing of speech signals in terms of time and frequency of the phases. It is an Automatic Speech Recognition (ASR) algorithm.
What Is the Purpose of Speech Processing?
The fundamental purpose of Speech Processing is the transmission of messages or communication. But that is not the only purpose behind processing speech. It has other Some of the critical objectives of Speech Processing are:
- To recognize speech as a means of communication
- To represent speech for transmission and reproduction
- To analyze speech for automatic speech recognition
- To extract information by analyzing speech
- To analyze and discover some physiological characteristics of the speaker’s voice
- To remove noises and enhance audio quality over telephonic calls
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