期刊
INTERNATIONAL JOURNAL OF APPLIED PATTERN RECOGNITION
卷 5, 期 2, 页码 137-148出版社
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJAPR.2018.092522
关键词
mel-frequency cepstrum coefficients; MFCC; perceptual linear prediction; PLP; linear prediction cepstral coefficients; LPCC; speech features; phonetic engine; hidden Markov model; HMM; HTK toolkit
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.
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