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Few-Shot learning for frame-Wise phoneme recognition: Adaptation of matching networks

Recently, the topic of Few-Shot Learning (FSL) is emerging as a radical direction in machine learning, well established with a variety of paradigms and network realizations for image recognition. However, FSL is yet to emerge in speech recognition and allied topics. In this paper, we adapt an FSL paradigm ‘matching networks' to the problem of speech recognition, in a first of its kind attempt, to different tasks such as multi-speaker small-to-medium vocabulary word recognition, monolingual and cross-lingual phoneme recognition tasks under mel-spectrogram and single-frame feature representations. The key to FSL is the ability to use extremely small number of training utterances, e.g. as low as 1 exemplar / class, as a N-way K-shot learning problem for large N and very small K (e.g. K=1 to 10). We show a remarkably high performance for each of the different speech recognition tasks considered here with matching networks, consistently requiring only very few ‘shots' of exemplars/class, even while surpassing the performance of a direct application of KDE (kernel density estimation) without the matching network's embedding. This adaptation sets the basis for applying the matching network framework to continuous speech recognition and cross-lingual ASR with extremely low training requirements in the target test language.