Optimization of data-driven filterbank for automatic speaker verification

  title={Optimization of data-driven filterbank for automatic speaker verification},
  author={Susanta Kumar Sarangi and Md. Sahidullah and Goutam Saha},
Abstract Most of the speech processing applications use triangular filters spaced in mel-scale for feature extraction. In this paper, we propose a new data-driven filter design method which optimizes filter parameters from a given speech data. First, we introduce a frame-selection based approach for developing speech-signal-based frequency warping scale. Then, we propose a new method for computing the filter frequency responses by using principal component analysis (PCA). The main advantage of… Expand
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A novel approach in feature level for robust text-independent speaker identification system
  • S. Sarangi, G. Saha
  • Computer Science
  • 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)
  • 2012
Speech-signal-based frequency cepstral coefficients (SFCC) is introduced in speaker recognition domain and proposed to use combination of filter banks of both the MFCC and SFCC in text-independent speaker identification. Expand
Data-driven spectral basis functions for automatic speech recognition
Stochastic methods for designing feature extraction methods which are trained to alleviate the unwanted variability present in speech signals are proposed and shown to provide significant advantages over the conventional methods both in terms of performance of ASR and in providing understanding about the nature of speech signal. Expand
Improved Closed Set Text-Independent Speaker Identification by Combining MFCC with Evidence from Flipped Filter Banks
A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years,Expand
Optimization of temporal filters for constructing robust features in speech recognition
  • J. Hung, Lin-Shan Lee
  • Mathematics, Computer Science
  • IEEE Transactions on Audio, Speech, and Language Processing
  • 2006
It was found that the new optimization criteria of principal component analysis (PCA) and the minimum classification error (MCE) for constructing the temporal filters lead to superior performance over the original MFCC features, just as LDA-derived filters can. Expand
Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition
A class of linear transformation techniques based on block wise transformation of MFLE which effectively decorrelate the filter bank log energies and also capture speech information in an efficient manner are studied. Expand
Mean Hilbert envelope coefficients (MHEC) for robust speaker and language identification
Experimental results indicate that: (i) the MHEC feature is highly effective and performs favorably compared to other conventional and state-of-the-art front-ends, and (ii) the power-law non-linearity consistently yields the best performance across different conditions for both SID and LID tasks. Expand
Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition
  • Chanwoo Kim, R. Stern
  • Computer Science
  • IEEE/ACM Transactions on Audio, Speech, and Language Processing
  • 2016
Experimental results demonstrate that PNCC processing provides substantial improvements in recognition accuracy compared to MFCC and PLP processing for speech in the presence of various types of additive noise and in reverberant environments, with only slightly greater computational cost than conventional MFCC processing. Expand
Data Driven Design of Filter Bank for Speech Recognition
This work presents a method where the filter bank, optimized for discriminability between phonemes, is derived directly from phonetically labeled speech data using Linear Discriminant Analysis, proving the fact that incorporation of psychoacoustic findings into feature extraction can lead to better recognition performance. Expand
A perceptually-motivated low-complexity instantaneous linear channel normalization technique applied to speaker verification
Speaker verification results demonstrate that the proposed LNCC features are of low computational complexity and far more effectively compensate for spectral tilt than ordinary MFCC coefficients. Expand
Data-Driven Temporal Filters and Alternatives to GMM in Speaker Verification
A novel method for designing filters that are capable of normalizing the variability introduced by different telephone handsets is introduced and the effectiveness of the proposed channel normalizing filter in improving speaker verification performance in mismatched conditions is demonstrated. Expand