CONF tsamuel:eusipco:2008/IDIAP Spectro-Temporal Features for Automatic Speech Recognition using Linear Prediction in Spectral Domain Thomas, Samuel Ganapathy, Sriram Hermansky, Hynek EXTERNAL https://publications.idiap.ch/attachments/papers/2008/tsamuel-eusipco-2008.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/tsamuel:rr08-05 Related documents EUSIPCO 2008 2008 IDIAP-RR 08-05 Frequency Domain Linear Prediction (FDLP) provides an efficient way to represent temporal envelopes of a signal using auto-regressive models. For the input speech signal, we use FDLP to estimate temporal trajectories of sub-band energy by applying linear prediction on the cosine transform of sub-band signals. The sub-band FDLP envelopes are used to extract spectral and temporal features for speech recognition. The spectral features are derived by integrating the temporal envelopes in short-term frames and the temporal features are formed by converting these envelopes into modulation frequency components. These features are then combined in the phoneme posterior level and used as the input features for a hybrid HMM-ANN based phoneme recognizer. The proposed spectro-temporal features provide a phoneme recognition accuracy of $69.1 \%$ (an improvement of $4.8 \%$ over the Perceptual Linear Prediction (PLP) base-line) for the TIMIT database. REPORT tsamuel:rr08-05/IDIAP Spectro-Temporal Features for Automatic Speech Recognition using Linear Prediction in Spectral Domain Thomas, Samuel Ganapathy, Sriram Hermansky, Hynek EXTERNAL https://publications.idiap.ch/attachments/reports/2008/tsamuel-idiap-rr-08-05.pdf PUBLIC Idiap-RR-05-2008 2008 IDIAP To appear in EUSIPCO 2008 Frequency Domain Linear Prediction (FDLP) provides an efficient way to represent temporal envelopes of a signal using auto-regressive models. For the input speech signal, we use FDLP to estimate temporal trajectories of sub-band energy by applying linear prediction on the cosine transform of sub-band signals. The sub-band FDLP envelopes are used to extract spectral and temporal features for speech recognition. The spectral features are derived by integrating the temporal envelopes in short-term frames and the temporal features are formed by converting these envelopes into modulation frequency components. These features are then combined in the phoneme posterior level and used as the input features for a hybrid HMM-ANN based phoneme recognizer. The proposed spectro-temporal features provide a phoneme recognition accuracy of $69.1 \%$ (an improvement of $4.8 \%$ over the Perceptual Linear Prediction (PLP) base-line) for the TIMIT database.