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Applying Human—Computer Interaction Practices to IoT Prototyping
Published in Hongjian Sun, Chao Wang, Bashar I. Ahmad, From Internet of Things to Smart Cities, 2017
Salim. Haniff, Markku. Turunen, Roope. Raisamo
Research into the field of Automated Speech Recognition (ASR) can be dated as far back as the 1930s by work done by Homer Dudley of Bell Laboratories [19]. From the 1930s to today, a lot of advancements have taken place in the area of ASR, which overlaps the 1980s demo by Richard A. Bolt. The use of statistical models, like Hidden Markov Model (HMM), and the utilization of Machine Learning and Artificial Intelligence has improved the recognition rates of ASR systems. One well known open source project used in ASR research is CMUSphinx (https://cmusphinx.sourceforge.net/). The coming of Sphinx-4 from the CMUSphinx project brings the use of state-of-theart HMM based speech recognition system [21]. Another open source ASR toolkit is Kaldi (https://kaldi-asr.org/), which was designed to help speech recognition researchers [29]. Kaldi could be coupled with GStreamer, so audio from a device can be sent to a server running Kaldi-GStreamer for remote speech recognition, then the recognized speech could be sent back to the remote device [3].
Automatic Speech Recognition Using Limited Vocabulary: A Survey
Published in Applied Artificial Intelligence, 2022
Jean Louis K. E Fendji, Diane C. M. Tala, Blaise O. Yenke, Marcellin Atemkeng
A former toolkit that may no longer be available is the Rapid Language Adaptation Toolkit (RLAT) introduced in (Schultz 2009) and used in (Vu et al. 2010). The website for the project is, unfortunately, no longer available. Kaldi is an extendable and modular toolkit for ASR (Povey 2011). The large community behind the project provides numerous third-party modules that can be used for several tasks. It supports DNN and offers excellent documentation on its website. Several works have been based on this toolkit including (Guglani and Mishra 2018).