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The team is involved in teaching, see: ITI-PSL Cognitive Engineering.


The bootphon team develops pipelines for data analysis, speech processing or machine learning and distribute them in an open source format in the bootphon repo on github.

The ABXpy package

This package computes an ABX discrimination score on a large database of tokens encoded with domain-specific features. It assumes that the tokens have particular properties (listed in an item file), and that the features (listed in a feature file) can be compared using a particular distance metric.

The ABX task consists in presenting three tokens: A, B, and X, and deciding wether the distance between A and X is greater or smaller than the distance between B and X. The package runs a systematic evaluation of all of the ABX triplets that match particular constraints, and if the number is too large, it samples a smaller number of them.

This particular triplet tests for the /b/ vs /g/ contrast, across talkers T1 and T2, and the context is the vowel /a/. The package will likewise find all of the triplets of the same general shape.

The code, written in python, has been optimized for multicore processing and can compute the scores of around 1M ABX triplets of spoken word speech features in about 45 min on a 10 cores machine. The implemented distances are the euclidian, cosine and KL distances plus DTW for alignment of tokens with variable length feature matrices as in speech. See Schatz et al. (2013; 2014) for applications with the evaluation of speech features.

This package can be downloaded on github.


Articulation Index upgrade

... under construction ...

Buckeye corpus speech recognition layer

... under construction ...

Other zero resource tools

Here is a list of papers and open source implementations of these papers regarding unsupervised speech learning. This is given for documentation purposes without any warranty that these implementations will actually work or do anything on a new corpus. However, we are very interested in large scale testing and evaluation of these algorithms. Please report to us what you've found.

Discovery of subword units or subword representations

Spoken Term Discovery