Scalable distributed speech recognition using Gaussian mixture model-based block quantisation

So, Stephen; Paliwal, Kuldip K.

doi:10.1016/j.specom.2005.10.002

Cited by 15 publications

(2 citation statements)

References 32 publications

(44 reference statements)

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“…They include manual identification and are therefore able to generate accurate results. Stephen So et al have worked on sound classifiers based on Gaussian mixture models (GMMs) and leverage MFCC to improve the identification performance of the algorithms [7]. Bittle and Duncan et al incorporate the support vector machine (SVM) sound target classification technology in sound source identification and classify the signal based on the similarity between the unknown signal and the model training data [8].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Acoustic Source via MFF-ResNet with Low Sample Complexity

Cui

Liu

et al. 2022

Electronics

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Acoustic signal classification plays a central role in acoustic source identification. In practical applications, however, varieties of training data are typically inadequate, which leads to a low sample complexity. Applying classical deep learning methods to identify acoustic signals involves a large number of parameters in the classification model, which calls for great sample complexity. Therefore, low sample complexity modeling is one of the most important issues related to the performance of the acoustic signal classification. In this study, the authors propose a novel data fusion model named MFF-ResNet, in which manual design features and deep representation of log-Mel spectrogram features are fused with bi-level attention. The proposed approach involves an amount of prior human knowledge as implicit regularization, thus leading to an interpretable and low sample complexity model of the acoustic signal classification. The experimental results suggested that MFF-ResNet is capable of accurate acoustic signal classification with fewer training samples.

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Section: Introductionmentioning

confidence: 99%

Identifying the Acoustic Source via MFF-ResNet with Low Sample Complexity

Cui

Liu

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…Several coding methods for compressing MFCCs have been proposed in literature (Srinivasamurthy et al, 2006;Kiss, 2000;Zhu and Alwan, 2001;Hirsch, 1998;So and Paliwal, 2006;Borgstrom and Alwan, 2007;Digalakis et al, 1999;Ramaswamy and Gopalakrishnan, 1998;Kiss and Kapanen, 1999). In early attempts at compression, scalar quantization and vector quantization were applied to MFCCs, and the word error rates (WERs) were measured according to various bit-rates (Digalakis et al, 1999).…”

Section: Introductionmentioning

confidence: 99%