Speech processing for under-resourced languages is an active field of research, which has experienced significant progress during the past decade. We propose, in this paper, a survey that focuses on automatic speech recognition (ASR) for these languages. The definition of under-resourced languages and the challenges associated to them are first defined. The main part of the paper is a literature review of the recent (last 8 years) contributions made in ASR for under-resourced languages. Examples of past projects and future trends when dealing with under-resourced languages are also presented. We believe that this paper will be a good starting point for anyone interested to initiate research in (or operational development of) ASR for one or several under-resourced languages. It should be clear, however, that many of the issues and approaches presented here, apply to speech technology in general (text-to-speech synthesis for instance).

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The field of paralinguistics is growing rapidly with a wide range of applications that go beyond recognition of emotions, laughter and personality. The research flourishes in multiple directions such as signal representation and classification, addressing the issues of the domain. Apart from the noise robustness, an important issue with real life data is the imbalanced nature: some classes of states/traits are under-represented. Combined with the high dimensionality of the feature vectors used in the state-of-the-art analysis systems, this issue poses the threat of over-fitting. While the kernel trick can be employed to handle the dimensionality issue, regular classifiers inherently aim to minimize the misclassification error and hence are biased towards the majority class. A solution to this problem is oversampling of the minority class(es). However, this brings increased memory/computational costs, while not bringing any new information to the classifier. In this work, we propose a new weighting scheme on instances of the original dataset, employing Weighted Kernel Extreme Learning Machine, and inspired from that, introducing the Weighted Partial Least Squares Regression based classifier. The proposed methods are applied on all three INTERSPEECH ComParE 2017 challenge corpora, giving better or competitive results compared to the challenge baselines.