 Speech signal processing Speech recognition Speech synthesis Speech compression Speaker diarization and its applications  Image processing Image processing.

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Presentation transcript:

 Speech signal processing Speech recognition Speech synthesis Speech compression Speaker diarization and its applications  Image processing Image processing and its applications

 Speech signal processing refers to the acquisition, manipulation, storage, transfer and output of vocal utterances by a computer.  The main applications are the recognition, synthesis and compression of human speech.  Image processing is any form of signal processing for which the input is an image, such as a photograph or video frame the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two dimensional signal and applying standard signal-processing techniques to it.

What is signal processing? Signal processing is exactly what it says, it may be: –Amplifying –Filtering –Peak-clipping –Compression: output limiting, WDRC, etc –Frequency shifting

 Speech Recognition  Speech Synthesis  Speech Compression  Speaker Diarization

 Speech recognition (also called voice recognition) focuses on capturing the human voice as a digital sound wave and converting it into a computer-readable format

Speech synthesis is the reverse process of speech recognition. Advances in this area improve the computer's usability for the visually impaired.

Speech compression is important in the telecommunications area for increasing the amount of information which can be transferred, stored, or heard, for a given set of time and space constraints.

Speaker diarization is the process of determining who spoke when in a signal.

Speech recognition HHealthcare MMilitary HHigh-performance fighter aircraft HHelicopters BBattle management TTraining air traffic controllers TTelephony and other domains Speech synthesis has long been a vital assistive technology tool and its application in this area is significant and widespread. It allows environmental barriers to be removed for people with a wide range of disabilities. The longest application has been in the use of screen readers for people with visual impairment, but text-to- speech systems are now commonly used by people with dyslexia and other reading difficulties as well as by pre-literate children. They are also frequently employed to aid those with severe speech impairment usually through a dedicated voice output communication aid.

In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal- processing techniques to it.

1)Face detection Another case where image processing techniques are used is face detection. It is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Face detection can be regarded as a specific case of object-class detection; in object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.

Image Enhancement

Image Histograms Four basic image types: Dark, Bright, Low-contrast And High contrast images

Neighborhood Processing (filtering) Q: What happens if I reshuffle all pixels within the image? A: It’s histogram won’t change. No point processing will be affected…

speech processing system needs to either: –Separate the “uninteresting” sources of variability from the “interesting” one(s) OR –Work in limited conditions. Example: speech recognition: fixed speaker, task, and environment speaker recognition: fixed linguistic content, task, and environment So, with the above said stages and techniques, digital image can be made noise free and it can be made available in any desired format. (X-rays, photo negatives, improved image, etc)