Performance of a speech recognition system is highly dependent on the operational environments. The mismatched ambient conditions have adverse impact on the performance of an Automatic Speech Recognition (ASR) system. The speech parameterization techniques for tonal speech recognition are different from those used for non-tonal speech recognition. It is due to the fact that tonal speech has two components – basic linguistic unit and tone. The basic linguistic unit with different tones convey different meanings. Therefore, the feature set used for tonal speech recognition must have the capability to representing both of them. Tone is determined by the fundamental frequency of the speech signal which is highly sensitive to noise. Since at the time of parameterization of the non-tonal speech recognition systems, these highly noise-sensitive tone related information are discarded, the traditional noise elimination methods used for non-tonal speech recognition fail to deliver robust performance in tonal speech recognition. In the present study, we have analyze the performance of different commonly used feature sets for noisy tonal speech recognition. Hidden Markov Model (HMM) based speech recognizer has been used for performance evaluation. Noise elimination techniques sub-band spectral subtraction and Wiener filter have been used for noise reduction and their relative performance have been evaluated.
Noise-Robust Speech Recognition System based on Multimodal Audio-Visual Approach Using Different Deep Learning Classification Techniques
