Spectro-temporal analysis of speech for Spanish phoneme recognition

Sara Sharifzadeh*, Javier Serrano, Jordi Carrabina

*Autor corresponent d’aquest treball

Producció científica: Capítol de llibreCapítolRecerca

5 Cites (Scopus)

Resum

State of the art speech recognition systems (ASR), mostly use Mel-Frequency cepstral coefficients (MFCC), as acoustic features. In this paper, we propose a new discriminative analysis of acoustic features, based on spectrogram analysis. Both spectral and temporal variations of speech signal are considered. This has improved the recognition performance especially in case of noisy situation and phonemes with time domain modulations such as stops. In this method, the 2D Discrete Cosine Transform (DCT) is applied on small overlapped 2D Hamming windowed patches of spectrogram of Spanish phonemes and enhanced by means of bi-cubic interpolation. An adaptive strategy is proposed for the size of patches over the time to construct unique length vectors for different phonemes. These vectors are classified based on K-nearest neighbor (KNN) and linear discriminative analysis (LDA) and reduced rank LDA (RLDA). Experimental results demonstrate improvement in recognition performance for noisy speech signals and stops.

Idioma originalAnglès
Títol de la publicació2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012
Pàgines548-551
Nombre de pàgines4
Edició1
Estat de la publicacióPublicada - 1 de gen. 2012

Sèrie de publicacions

Nom2012 19th International Conference on Systems, Signals and Image Processing, IWSSIP 2012

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