Section: New Results

Development of cognitively inspired algorithms

Speech and language processing in humans infants and adults is particularly efficient. We use these as sources of inspiration for developing novel machine learning and speech technology algorithms. In this area, our results are as follows:

• In [22], we summarize the accomplishments of a multi-disciplinary 6-weeks workshop organized by E. Dupoux (PI) at Carnegy Mellon Univerrsity (Pittsburgh), funded through the Jelinek Memorial Summer Workshop Program of Johns Hopkins University. The workshop explored the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We studied the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsupervised discovery from raw speech.

• Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising results on artificial examples but still lack of in situ experiments. In [20] , we apply state-of-the-art Bayesian models to unsupervised Acoustic Unit Discovery (AUD) in a real low-resource language scenario. We also show that Bayesian models can naturally integrate information from other resourceful languages by means of informative prior leading to more consistent discovered units. Finally, discovered acoustic units are used, either as the 1-best sequence or as a lattice, to perform word segmentation. Word segmentation results show that this Bayesian approach clearly outperforms a Segmental-DTW baseline on the same corpus.

• Fixed-length embeddings of words are very useful for a variety of tasks in speech and language processing. In [19], we systematically explore two methods of computing fixed-length embeddings for variable-length sequences. We evaluate their susceptibility to phonetic and speaker-specific variability on English, a high resource language, and Xitsonga, a low resource language, using two evaluation metrics: ABX word discrimination and ROC-AUC on same-different phoneme n-grams. We show that a simple downsampling method supplemented with length information can be competitive with the variable-length input feature representation on both evaluations. Recurrent autoencoders trained without supervision can yield even better results at the expense of increased computational complexity.

• Recent studies have investigated siamese network architectures for learning invariant speech representations using same-different side information at the word level. In [21], we investigate systematically an often ignored component of siamese networks: the sampling procedure (how pairs of same vs. different tokens are selected). We show that sampling strategies taking into account Zipf's Law, the distribution of speakers and the proportions of same and different pairs of words significantly impact the performance of the network. In particular, we show that word frequency compression improves learning across a large range of variations in number of training pairs. This effect does not apply to the same extent to the fully unsupervised setting, where the pairs of same-different words are obtained by spoken term discovery. We apply these results to pairs of words discovered using an unsupervised algorithm and show an improvement on state-of-the-art in unsupervised representation learning using siamese networks.

• Unsupervised spoken term discovery is the task of finding recurrent acoustic patterns in speech without any annotations. Current approaches consists of two steps: (1) discovering similar patterns in speech, and (2) partitioning those pairs of acoustic tokens using graph clustering methods. In, [23] we propose a new approach for the first step. Previous systems used various approximation algorithms to make the search tractable on large amounts of data. Our approach is based on an optimized $k$-nearest neighbours (KNN) search coupled with a fixed word embedding algorithm. The results show that the KNN algorithm is robust across languages, consistently outperforms the DTW-based baseline, and is competitive with current state-of-the-art spoken term discovery systems.