A comparison of three spectral features for phone recognition in sub-optimal environments

This paper presents a comparison of three spectral features for automatic phone recognition in sub-optimal environments. An exclusive study is carried out with a phone recognition system called phonetic engine (PE) developed in the Manipuri language. The Manipuri language is a scheduled Indian language being used as the official language in the State of Manipur. However, there is no standard database of the language so far. Therefore, a PE has been built for this language. Here phonetic transcriptions are done and then modeling of each phonetic unit is carried out using hidden Markov model (HMM). Speech feature extraction is a very important stage in the development of such a PE. An analysis of phone recognition accuracies of the PE due the three dominant spectral features: MFCC, PLP and LPCC have been studied here. It is found that PLP and MFCC outperform LPCC features under all circumstances.