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.

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Call center operators communicate with callers in different emotional states (anger, anxiety, fear, stress, joy, etc.). Sometimes a number of calls coming in a short period of time have to be answered and processed. In the moments when all call center operators are busy, the system puts that call on hold, regardless of its urgency. This research aims to improve the functionality of call centers by recognition of call urgency and redistribution of calls in a queue. It could be beneficial for call centers giving health care support for elderly people and emergency call centers. The proposed recognition of call urgency and consequent call ranking and redistribution is based on emotion recognition in speech, giving greater priority to calls featuring emotions such as fear, anger and sadness, and less priority to calls featuring neutral speech and happiness. Experimental results, obtained in a simulated call center, show a significant reduction in waiting time for calls estimated as more urgent, especially the calls featuring the emotions of fear and anger.

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