Formant weighted cepstral feature for LSP-based speech recognition
نویسندگان
چکیده
In this paper, we propose a formant weighted cepstral feature for LSP-based speech recognition system. The proposed weighting scheme is based on the well-known property of LSPs that the speech spectrum has a peak when adjacent LSFs come close. By applying this scheme to pseudo-cepstrum (PCEP) conversion process [1], we can obtain formant weighted or peak enhanced cepstral feature. Results of speech recognition experiments using QCELP coder output show that the proposed feature set outperforms the conventional features such as LSP or PCEP. Moreover its performance also exceeds that of unquantized LPC cepstrum.
منابع مشابه
LSP weighting functions based on spectral sensitivity and mel-frequency warping for speech recognition in digital communication
In digital communication networks, a speech recognition system extracts feature parameters after reconstructing speech signals. In this paper, we consider a useful approach of incorporating speech coding parameters into a speech recognizer. Most speech coders employ line spectrum pairs (LSPs) to represent spectral parameters. We introduce weighted distance measures to improve the recognition pe...
متن کاملA study of line spectrum pair frequencies for vowel recognition
The line spectrum pair (LSP) frequency represer.iation has recent:y been proposed as an alternative linear prediction (LP) parametric representation. In the context of speech coding, this representation shows better quantization properties than the other LP parametric representations. In the present paper, the LSP representation is studied for speech recognition. Several distance measures based...
متن کاملAutomatic Speech Recognition in GSM Network Using the Bit-Stream and Auxiliary parameters
The Global System for Mobile (GSM) environment includes three main problems for Automatic Speech Recognition (ASR) systems: noisy scenarios, source coding distortion and transmission errors.The second, source coding distortion must be explicitly addressed.The front-end of the speech recognition system combines feature extracted by converting the quantized spectral information of speech coder, p...
متن کاملImproving of Feature Selection in Speech Emotion Recognition Based-on Hybrid Evolutionary Algorithms
One of the important issues in speech emotion recognizing is selecting of appropriate feature sets in order to improve the detection rate and classification accuracy. In last studies researchers tried to select the appropriate features for classification by using the selecting and reducing the space of features methods, such as the Fisher and PCA. In this research, a hybrid evolutionary algorit...
متن کاملFormant position based weighted spectral features for emotion recognition
In this paper, we propose novel spectrally weighted mel-frequency cepstral coefficient (WMFCC) features for emotion recognition from speech. The idea is based on the fact that formant locations carry emotion-related information, and therefore critical spectral bands around formant locations can be emphasized during the calculation of MFCC features. The spectral weighting is derived from the nor...
متن کامل