Python Hotword Detection
Project description
This library provides functionality for detecting a hotword in given audio file using MFCC features and Dynamic Time Warping (DTW) pattern matching algorithm.
Installation
This project is on pypi
To install from pypi:
pip install hotword_detection
From this repository:
git clone https://github.com/sakethgsharma/HotWordDetection.git python setup.py install
Usage
Example scripts
For training a hotword, run:
python bin/trainHotword.py
For testing, run:
python bin/checkHotword.py
Supported features
Mel Frequency Cepstral Coefficients
Choice of selecting any suitable hot word through appropriate training paradigm
Supports variable sampling frequencies
Amplitude based Voice Activity Detector(VAD) used during recordings to remove extraneous noise
Personalization using automatic DTW thresholding
MFCC Features
MFCC vectors are used in this module since they are the most commonly extracted features used for speech recognition systems.
Parameter |
Description |
|---|---|
alpha |
Parameter used in pre-emphasis filtering. Should be any value between 0 and 1. |
N |
Number of FFT points. |
fs |
Sampling frequency of stored audio file. |
frame_dur |
Duration of 1 speech frame. |
num_filters |
Number of filters used in the Mel filterbank. |
lower_freq |
Lower frequency bound used for constructing filterbank. |
upper_freq |
Upper frequency bound used for constructing filterbank. Should be less than fs/2. |
Dynamic Time Warping
Dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences which may vary in speed.
Reference
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