====================== HotWord Detection

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 <https://pypi.python.org/pypi/hotword_detection/1.2>_

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

MFCC tutorial <http://www.practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/>_ DTW Wiki <http://en.wikipedia.org/wiki/Dynamic_time_warping>_