I have created a stand-alone Matlab demo of the noise-robust Automatic Speech Recognition (ASR) techniques I worked on since 2007. All these techniques rely on finding a sparse, linear combination of noise-free speech exemplars, which is then either used to make an estimate of the clean speech, or to perform exemplar based ASR.Highlights:
- AURORA-2 noise robust digit recognition
- Supports multiple methods: Sparse Imputation, Feature Enhancement (FE), Sparse Classification (SC), Hybrid SC/FE (SCFE).
- Includes a MATLAB word-based ASR system
- GPU acceleration
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Missing data mask estimation is the process of estimating which spectro-temporal regions in a spectrographic representation of noisy speech remain (relatively) uncorrupted. I have compiled an Octave/MATLAB package for machine-learning (SVM) based missing data mask estimation.
- Machine learning approach based on a training set of noisy speech with known underlying speech and noise
- The SVM classifier exploits many different features, such as long-term noise floors and harmonicity
- The package supports hyperparameter optimization, training and evaluation
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Audio event detection
Top-ranking contribution to the AASP acoustic event detection challenge. You can grab the code here.
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Weakly supervised learning of acoustic units
I have created a stand-alone Matlab demo of the research on using Non-negative Matrix Factorisation (NMF) to learn acoustic units (such as words, phrases, or acoustic events) with only weakly annotated material: The audio samples are annotated only with tags indicating the presence of a word or event, without segmentation or temporal ordering.