With the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-resource languages, presenting three main difficulties for the development of ASR systems: (1) the scarcity of the data; (2) the uncertainty in the writing and pronunciation; (3) the individuality of each language. Uyghur, Kazakh, and Kyrgyz as examples are all low-resource languages, involving clear geographical variation in their pronunciation, and each language possesses its own unique acoustic properties and phonological rules. On the other hand, they all belong to the Altaic language family of the Altaic branch, so they share many commonalities. This paper presents an overview of speech recognition techniques developed for Uyghur, Kazakh, and Kyrgyz, with the purposes of (1) highlighting the techniques that are specifically effective for each language and generally effective for all of them and (2) discovering the important factors in promoting the speech recognition research of low-resource languages, by a comparative study of the development path of these three neighboring languages.

This paper investigates the fixed-time (FXT) and predefined-time (PDT) synchronization of memristive neural networks (MNNs) subject to stochastic disturbances, reaction-diffusion terms, and time delays. First, a new PDT stability criterion is established for stochastic nonlinear systems, which permits a priori assignment of the settling time bound regardless of initial conditions, and offers a more concise form than prior results. Second, by leveraging Green’s formula, integral inequality, and stochastic analysis, some sufficient conditions are derived to guarantee FXT and PDT synchronization of introduced stochastic MNNs with reaction-diffusion terms. Finally, numerical simulations are given to validate the effectiveness of the proposed synchronization scheme.

Deep learning has significantly boosted the performance improvement of automatic speech recognition (ASR) with the cooperation of large amounts of data resources. For minority languages, however, there are almost no large-scale data resources, limiting the development of ASR technologies in these languages. In this paper, we publish a free Kirghiz speech database accompanied by associated language resources. The entire database involves 128 h of speech data from 163 speakers and corresponding transcriptions. To our knowledge, this is the largest Kirghiz speech database that is dedicated to the ASR task and is publicly free so far. In addition, we also provide several baseline systems based on Kaldi and WeNet to demonstrate how these public data resources can be used to facilitate the Kirghiz ASR research. This publication is a part of the M2ASR project, and all the resources can be downloaded at the project webpage.