Admissible wavelet packet sub-band-based harmonic energy features for Hindi phoneme recognition

In recent years, wavelet packet (WP) transform has been used as an important speech representation tool. WP-based acoustic features have found to be more effective than the short-time Fourier transform (STFT)-based features to capture the information of unvoiced phoneme in continuous speech. However, wavelet features fail to carry the same usefulness to represent the voiced phonemes such as vowels, nasals. This paper proposes new WP sub-band-based features by taking care of harmonic information of voiced speech signal. It has been noted that most of the voiced energy of the speech signal lies in between 250 and 2000 Hz. Thus, the proposed technique emphasises the individual sub-band harmonic energy up to 2 kHz. The speech signal is decomposed into 16 wavelet sub-bands and harmonic energy features are combined with WP cepstral (WPCC) features to enhance the performance of voiced phoneme recogniser. A standard phonetically balanced Hindi database is taken to analyse the performance of the proposed feature set. The noisy phoneme recognition task is also carried out to study the robustness. Significant improvement is obtained with the proposed feature set in voiced phoneme recognition over WPCC and conventional Mel frequency cepstral coefficient.