[month] [year]

B T Nellore – Speech Signal Processing

November 2022

Bhanu Teja Nellore received his Master of Science in  Electronics and Communication Engineering (ECE).  His research work was supervised by Prof. Yegnanaryana.  Here’s a summary of his research work on Applying Production Knowledge to Speech Signal Processing:

Speech is the most convenient and expressive form of communication among humans. Since the past few decades there have been continuous efforts from the technology community around the world to establish speech based human-computer interaction. In this respect, several speech based technologies including (but not limited to) automatic speech recognition, text to speech synthesis, speaker recognition, speaker verification and emotion recognition have evolved. Among the various natural signals that can be recorded using sensors (such as seismic signal, radio signals etc.), we have physiological understanding of the corresponding production process of only a few signals such as speech and cardiac signal. In this thesis, we explore the possibility of extracting sound information from speech using it’s corresponding production characteristics. In particular, certain acoustic features are proposed for the identification of stops and nasals in continuous speech. These acoustic features extract the corresponding sound’s production characteristics at two levels, namely, excitation source level and vocal tract system level. The acoustic features are extracted only at glottal closure instants using recently proposed signal processing techniques. The proposed algorithms do not rely on training on a database, and hence can be used for analysis on under-resourced languages. Further another study is presented to assess the individual contributions of magnitude and phase spectra of a given speech signal in preserving it’s intelligibility under different noise conditions. The results in this study can be used to develop algorithms for modifying the speech signal such that its intelligibility is improved in noise.