Spectro-Temporal Analysis of Speech for Spanish Phoneme Recognition

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

View graph of relations

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.
Original languageEnglish
Title of host publicationProceedings : IWSSIP 2012, 11-13 April 2012, Vienna, Austria
Publication date2012
ISBN (print)978-3-200-02588-2
StatePublished - 2012
Event19th International Conference on Systems, Signals and Image Processing (IWSSIP 2012) - Vienna, Austria


Conference19th International Conference on Systems, Signals and Image Processing (IWSSIP 2012)
Internet address

Bibliographical note

Best Student Paper in the Field of Speech processing.

Download as:
Download as PDF
Select render style:
Download as HTML
Select render style:
Download as Word
Select render style:

ID: 7991229