sr-lib

C++ Automatic Speech Recognition library for my BTech Project.

Features

• Isolated Word Recognition
• Word Prediction
• Sentence Recognition (using isolated words)
• Parallel and fast
• Configurable
• Portable

Results

• Test recordings are english digts (words) and few country codes (sentences)
• Accuracy for single speaker isolated word recognition: ~90%

Methods

• Audio Preprocessor
• Linear Predictive Cepstral Coefficients (LPCC)
• Mel Filter Cepstral Coefficients (MFCC)
• Derivative Cepstral Coefficients (delta, accel)
• Vector Quantisation (VQ) - LBG, KMeans
• Hidden Markov Model (HMM) - Baum–Welch, Viterbi
• DFA and NGram

Extras

• CSV Reader
• Wave Reader
• Logger
• Thread Pool
• Config Parser

How to build?

sr-lib doesn't depend on any libraries other than the standard ones.

For windows:

• Get Visual Studio 2017 (with CMake tools).
• Open sr-lib as a folder
• Click Generate cache from CMake menu
• Click Build all from CMake menu
• Select run target WordRecognition (preferably x64 release)
• Run to see the demo
• Profit???

For others:

• Use the command line :(

How to improve accuracy?

In general, isolated word recognition works well for:

• distinct words in the vocabulary
• short utterance durations
• fewer words in the vocabulary

These parameters can be fine tuned:

key	type	description
q_cache	bool	whether cached training files should be used
n_thread	int	number of threads used for parallel execution
q_trim	bool	whether the samples should be trimmed for background noise
x_frame	int	number of samples in a frame
x_overlap	int	number of samples to be overlapped while framing
cepstra	string	"mfc" or "lpc" variants of feature generation
n_cepstra	int	number of features
q_gain	bool	whether gain term should be added to features
q_delta	bool	whether delta terms should be added to features
q_accel	bool	whether accel terms should be added to features
x_codebook	int	size of codebook
n_state	int	number of states in HMM
n_bakis	int	connentedness of initial bakis model for HMM
n_retrain	int	number of times each model should be trained
n_gram	int	number of previous words to be considered for prediction
q_dfa	bool	command based word prediction or probability based
gram_weight	double	linear weight for the final scoring with recognition result
cutoff_score	double	cutoff for final score