Neural Speech Recognition Lecture
Acoustic digital signals are ubiquitous and have many applications in several disciplines, notably in:
-Digital Media, Social Media (music, voice signals),
-Biomedical Signal Analysis and Diagnosis,
-Scientific signal acquisition of any sort, e.g., Environment Sensing, Geophysical Prospecting.
Text can be considered as 1D signal, as it evolves over time (actually as an order time series of letters or words). Its analysis is extremely important in social media applications (e.g., Tweet analysis), in the analysis of any written and/or broadcasted text (e.g., news articles in newspapers) and in literary text analysis.

This lecture overviews Neural Speech Recognition is a special case of Automatic Speech Recognition (ASR), i.e., the transcription of speech to text that has many applications e.g., in call centers, dictation, meeting minutes creation, Smart assistants (Apple’s Siri, Amazon’s Alexa, Google Assistant, Microsoft’s Cortana) and in Behavior /emotion recognition. It covers the following topics in detail: Neural Speech Recognition Datasets. Neural Speech Recognition Methods. Deep Neural Network (RNN, CNN, Transformer) methods.