The Usefulness of Imperfect Speech Data for ASR Development in Low-Resource Languages

Abstract

When the National Centre for Human Language Technology (NCHLT) Speech corpus was released, it created various opportunities for speech technology development in the 11 official, but critically under-resourced, languages of South Africa. Since then, the substantial improvements in acoustic modeling that deep architectures achieved for well-resourced languages ushered in a new data requirement: their development requires hundreds of hours of speech. A suitable strategy for the enlargement of speech resources for the South African languages is therefore required. The first possibility was to look for data that has already been collected but has not been included in an existing corpus. Additional data was collected during the NCHLT project that was not included in the official corpus: it only contains a curated, but limited subset of the data. In this paper, we first analyze the additional resources that could be harvested from the auxiliary NCHLT data. We also measure the effect of this data on acoustic modeling. The analysis incorporates recent factorized time-delay neural networks (TDNN-F). These models significantly reduce phone error rates for all languages. In addition, data augmentation and cross-corpus validation experiments for a number of the datasets illustrate the utility of the auxiliary NCHLT data.

NCHLT Auxiliary speech data for ASR technology development in South Africa

“… The data contains additional examples of speech and provides more examples of languages recorded in varying acoustic conditions. It has been shown that combining this data set with existing speech data improves the recognition accuracy of ASR systems [2] . This is an important consideration, because there are currently almost no other resources available for speech technology development in South Africa.…”

IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

“…This can present one of the main research field challenges. It is known [1,31] from artificial intelligence and the Internet of Things that open data can stimulate the research area's development.…”

Lithuanian Speech Recognition Using Purely Phonetic Deep Learning

“…The computation complexity of the loss function is (| |). Note, however, that the element-wise multiplication, division, and log operation can be implemented in parallel with graphical parallel unit (GPU) at (1). In contrast, the implementation of CTC [48] based on a forward-backward algorithm has a computation complexity of ( • ), here is the sequence length and is the number of lokkahead steps.…”

“…Preservation of endangered languages may require speech recording, processing, and automatic recognition. Since the early days of modern computer science, automatic speech recognition (ASR) has been one of the biggest and hardest challenges in the field that requires huge volumes of speech data [1]. Over the years, a large majority of research conducted in this area focused on the most widely spoken languages, such as English, French, Mandarin Chinese, etc.…”