A comparative study of traditional and newly proposed features for recognition of speech under stress

By Sahar E. Bou-Ghazale, John H. L. Hansen. IEEE Transactions on Speech and Audio Processing, 2000

300 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2d14abde613f0c54e42bd1a44ab7283db3277108

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In Artificial Intelligence

In Experimental and Cognitive Psychology

In Signal Processing

Citations in the corpus

Dimensionality Reduction of a Pathological Voice Quality Assessment System Based on Gaussian Mixture Models and Short-Term Cepstral Parameters (2006, methodology): “However, FFT-based MFCCs typically encode more information from excitation; whereas LPC-based MFCCs remove this.”
Survey on speech emotion recognition: Features, classification schemes, and databases (2011, methodology): “Since human perception of pitch does not follow a linear scale [103], the filters’ bandwidths are usually evenly distributed with respect to a suitable nonlinear frequency scale such as the Bark scale [103], the Mel-frequency scale [103,61], the modified Mel-frequency scale, and the ExpoLog scale [1”
On Speech Recognition (2012, background): “Chia-yu et al. (2008a) proposed a new concept of context-dependent quantization in which the representative parameter (whether a scalar or a vector) for a quantization partition cell is not fixed, but depends on the signal context on both sides and the signal context dependencies can be trained with”
Eurospeech 2001-Scandinavia Robust Speech Recognition in Noise : An Evaluation using the SPINE Corpus † (2001, background): “However, the following references point to several works that are representative, though clearly not exhaustive[1] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13].”
Speech Emotion Recognition Using Fourier Parameters (2015, methodology): “Bou-Ghazale and Hansen [34] found that the features based on cepstral analysis, such as LPCC and MFCC, outperform the linear features of LPC in detecting speech emotions.”