| Title |
A bio-inspired feature extraction for robust speech recognition
|
|---|---|
| Published in |
SpringerPlus, November 2014
|
| DOI | 10.1186/2193-1801-3-651 |
| Pubmed ID | |
| Authors |
Youssef Zouhir, Kaïs Ouni |
| Abstract |
In this paper, a feature extraction method for robust speech recognition in noisy environments is proposed. The proposed method is motivated by a biologically inspired auditory model which simulates the outer/middle ear filtering by a low-pass filter and the spectral behaviour of the cochlea by the Gammachirp auditory filterbank (GcFB). The speech recognition performance of our method is tested on speech signals corrupted by real-world noises. The evaluation results show that the proposed method gives better recognition rates compared to the classic techniques such as Perceptual Linear Prediction (PLP), Linear Predictive Coding (LPC), Linear Prediction Cepstral coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC). The used recognition system is based on the Hidden Markov Models with continuous Gaussian Mixture densities (HMM-GM). |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 28% |
| Researcher | 2 | 11% |
| Student > Master | 2 | 11% |
| Student > Bachelor | 2 | 11% |
| Other | 1 | 6% |
| Other | 3 | 17% |
| Unknown | 3 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 39% |
| Engineering | 6 | 33% |
| Neuroscience | 1 | 6% |
| Agricultural and Biological Sciences | 1 | 6% |
| Unknown | 3 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2014.
All research outputs
#16,282,873
of 23,989,683 outputs
Outputs from SpringerPlus
#947
of 1,858 outputs
Outputs of similar age
#156,913
of 265,965 outputs
Outputs of similar age from SpringerPlus
#59
of 96 outputs
Altmetric has tracked 23,989,683 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,858 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 265,965 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.