C++ Automatic Speech Recognition library for my BTech Project.
- Isolated Word Recognition
- Word Prediction
- Sentence Recognition (using isolated words)
- Parallel and fast
- Configurable
- Portable
- Test recordings are english digts (words) and few country codes (sentences)
- Accuracy for single speaker isolated word recognition: ~90%
- 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
- CSV Reader
- Wave Reader
- Logger
- Thread Pool
- Config Parser
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 cachefrom CMake menu - Click
Build allfrom CMake menu - Select run target WordRecognition (preferably x64 release)
- Run to see the demo
- Profit???
For others:
- Use the command line :(
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 |
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