Proceedings ArticleDOI
Non-negative subspace projection during conventional MFCC feature extraction for noise robust speech recognition
D. S. Pavan Kumar,Raghavendra Bilgi,Srinivasan Umesh +2 more
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TLDR
An additional feature processing algorithm using Non-negative Matrix Factorization (NMF) is proposed to be included during the conventional extraction of Mel-frequency cepstral coefficients (MFCC) for achieving noise robustness in HMM based speech recognition.Abstract:
An additional feature processing algorithm using Non-negative Matrix Factorization (NMF) is proposed to be included during the conventional extraction of Mel-frequency cepstral coefficients (MFCC) for achieving noise robustness in HMM based speech recognition. The proposed approach reconstructs log-Mel filterbank outputs of speech data from a set of building blocks that form the bases of a speech subspace. The bases are learned using the standard NMF of training data. A variation of learning the bases is proposed, which uses histogram equalized activation coefficients during training, to achieve noise robustness. The proposed methods give up to 5.96% absolute improvement in recognition accuracy on Aurora-2 task over a baseline with standard MFCCs, and up to 13.69% improvement when combined with other feature normalization techniques like Histogram Equalization (HEQ) and Heteroscedastic Linear Discriminant Analysis (HLDA).read more
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Posted Content
Feature Normalisation for Robust Speech Recognition
TL;DR: An MLLR-based computationally efficient run-time noise adaptation method in SPLICE framework has been proposed, and a modification to the training process of SPLICE algorithm for noise robust speech recognition is proposed.
Book ChapterDOI
Stressed Speech Recognition Using Similarity Measurement on Inner Product Space
Bhanu Priya,Samarendra Dandapat +1 more
TL;DR: From experiment, it is observed that, stress information of stressed speech is not present in the complement cosine (1-cosine) times of stress speech on different inner product space.
References
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Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
Proceedings Article
Algorithms for Non-negative Matrix Factorization
Daniel D. Lee,H. Sebastian Seung +1 more
TL;DR: Two different multiplicative algorithms for non-negative matrix factorization are analyzed and one algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence.
Journal ArticleDOI
Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition
TL;DR: The results show that the hybrid system performed substantially better than source separation or missing data mask estimation at lower signal-to-noise ratios (SNRs), achieving up to 57.1% accuracy at SNR = -5 dB.
Journal ArticleDOI
Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
N. Kumar,Andreas G. Andreou +1 more
TL;DR: Theoretical results to the problem of speech recognition are applied and word-error reduction in systems that employed both diagonal and full covariance heteroscedastic Gaussian models tested on the TI-DIGITS database is observed.