Lip reading, also known as speech reading, is the ability to understand speech by observing a speaker's lip movements and facial expressions. It is a crucial skill for individuals who are deaf or hard of hearing, as well as for those who need to communicate in noisy environments. However, lip reading is a challenging task that requires extensive training and experience, and even then, it can be unreliable due to variations in lip movements and speech patterns.
Recent advances in machine learning and computer vision have made it possible to develop automatic lip reading systems that can recognize speech from visual cues alone. These systems have numerous applications, from enhancing speech recognition in noisy environments to improving the accessibility of multimedia content for the deaf and hard of hearing.
In this blog series, we will explore how to build a lip reading model using deep learning techniques. In Part 1, we will cover the basics of lip reading and the dataset we will be using for our model.
Understanding Lip Reading
Lip reading involves analyzing the movements of the mouth, tongue, and jaw to determine the sounds being produced by a speaker. There are several challenges associated with lip reading, including variations in speech patterns, differences in accent and dialect, and the fact that many sounds are produced inside the mouth and are not visible on the lips.
Despite these challenges, lip reading is a useful skill that can be learned with practice. It is typically taught using a combination of visual cues, such as the shape of the lips and the movement of the jaw, and contextual cues, such as the topic of conversation and the speaker's body language.
Dataset
The Grid Corpus dataset is a widely used dataset in the field of lip reading, which contains videos of people speaking a wide range of words. The dataset was created by recording videos of speakers reading out sentences while their faces were tracked using a facial landmark detection algorithm. The dataset contains over 30,000 video clips, each clip corresponding to a spoken sentence.
The videos in the dataset are captured at a resolution of 640x480 and a frame rate of 25 frames per second. The dataset also includes the corresponding audio for each video clip, as well as the transcriptions of the spoken sentences.
The Grid Corpus dataset has been used in numerous studies to develop and evaluate lip reading models. Some of the popular models developed using this dataset include LipNet and Watch, Listen, and Spell (WLAS).
Conclusion
In Part 1 of this blog series, we have introduced the concept of lip reading and the dataset we will be using to train our lip reading model. In the next part of the series, we will explore the preprocessing steps required to prepare the Lip Reading dataset for training, including data cleaning and feature extraction. Stay tuned for Part 2!
Top comments (1)
Hey! Stay Tuned for part 2