Military applications of automatic speech recognition and future requirements

An updated summary of the state-of-the-art of automatic speech recognition and its relevance to military applications is provided. A number of potential systems for military applications are under development. These include: (1) digital narrowband communication systems; (2) automatic speech verification; (3) on-line cartographic processing unit; (4) word recognition for militarized tactical data system; and (5) voice recognition and synthesis for aircraft cockpit

Language Modeling with Highway LSTM

Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks. In this paper, we extend an LSTM by adding highway networks inside an LSTM and use the resulting Highway LSTM (HW-LSTM) model for language modeling. The added highway networks increase the depth in the time dimension. Since a typical LSTM has two internal states, a memory cell and a hidden state, we compare various types of HW-LSTM by adding highway networks onto the memory cell and/or the hidden state. Experimental results on English broadcast news and conversational telephone speech recognition show that the proposed HW-LSTM LM improves speech recognition accuracy on top of a strong LSTM LM baseline. We report 5.1% and 9.9% on the Switchboard and CallHome subsets of the Hub5 2000 evaluation, which reaches the best performance numbers reported on these tasks to date.Comment: to appear in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2017

Mixture Density Networks, Human Articulatory Data and Acoustic-to-Articulatory Inversion of Continuous Speech

Researchers have been investigating methods for retrieving the articulation underlying an acoustic speech signal for more than three decades. A successful method would find many applications, for example: low bit-rate speech coding, helping individuals with speech and hearing disorders by providing visual feedback during speech training, and the possibility of improved automatic speech recognition