Stationary signal refers to signals whose statistical properties are constant within a certain region (in the case of ASR, inside a small window).

Stationary Signal

Signals whose statistical properties are not constant over time. For example, speech, in general, is a non-stationary signal.

Non-stationary Signal

Frame is the speech extracted from each window.

Frame

The windowing is characterized by three parameters: the window size or frame size of the window (its width in milliseconds), the frame stride, (also called shift or offset) between successive windows, and the shape of the window.

Parameters of Windowing

The rectangular window looks just like the original signal, except being extracted. However, abruptly cutting off the signal at its boundaries will create problems when we do Fourier analysis later.

Rectangular Window

A more commonly used windowing method for acoustic feature creation than rectangular windowing. Hamming window shrinks the values of the signal toward zero at the window boundaries, avoiding discontinuities.

Hamming Window

In feature extraction for ASR, we must extract spectral information from our windowed signal and find out how much energy the signal contains at different frequency bands. This is done with DFT.

Spectral Information Extraction

A small unit of extraction of features. From the digitized, quantized representation of the waveform, we need to extract spectral features from a small window of speech that characterizes part of a particular phoneme.

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Hamilton College

Quantization is the process of representing real-valued numbers as integers. Amplitude measurements are stored as integers, either 8 bit (values from -128– 127) or 16 bit (values from -32768–32767). We refer to each sample at time index n in the digitized, quantized waveform as x[n].


Quantization

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

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