Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments Article Swipe
Muskan Garg
,
Naveen Aggarwal
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.13100
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.13100
This research work is about recent development made in speech recognition. In this research work, analysis of isolated digit recognition in the presence of different bit rates and at different noise levels has been performed. This research work has been carried using audacity and HTK toolkit. Hidden Markov Model (HMM) is the recognition model which was used to perform this experiment. The feature extraction techniques used are Mel Frequency Cepstrum coefficient (MFCC), Linear Predictive Coding (LPC), perceptual linear predictive (PLP), mel spectrum (MELSPEC), filter bank (FBANK). There were three types of different noise levels which have been considered for testing of data. These include random noise, fan noise and random noise in real time environment. This was done to analyse the best environment which can used for real time applications. Further, five different types of commonly used bit rates at different sampling rates were considered to find out the most optimum bit rate.
Related Topics
Concepts
Speech recognition
Computer science
Linear predictive coding
Mel-frequency cepstrum
Hidden Markov model
Noise (video)
Pattern recognition (psychology)
Coding (social sciences)
Linear prediction
Artificial intelligence
Feature (linguistics)
Feature extraction
Cepstrum
Speech coding
Statistics
Mathematics
Philosophy
Image (mathematics)
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.13100
- https://arxiv.org/pdf/2208.13100
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293790420
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293790420Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2208.13100Digital Object Identifier
- Title
-
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environmentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-27Full publication date if available
- Authors
-
Muskan Garg, Naveen AggarwalList of authors in order
- Landing page
-
https://arxiv.org/abs/2208.13100Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2208.13100Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2208.13100Direct OA link when available
- Concepts
-
Speech recognition, Computer science, Linear predictive coding, Mel-frequency cepstrum, Hidden Markov model, Noise (video), Pattern recognition (psychology), Coding (social sciences), Linear prediction, Artificial intelligence, Feature (linguistics), Feature extraction, Cepstrum, Speech coding, Statistics, Mathematics, Philosophy, Image (mathematics), LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(MELSPEC), | 82 |
| abstract_inverted_index.Predictive | 73 |
| abstract_inverted_index.considered | 97, 144 |
| abstract_inverted_index.extraction | 63 |
| abstract_inverted_index.perceptual | 76 |
| abstract_inverted_index.performed. | 34 |
| abstract_inverted_index.predictive | 78 |
| abstract_inverted_index.techniques | 64 |
| abstract_inverted_index.coefficient | 70 |
| abstract_inverted_index.development | 6 |
| abstract_inverted_index.environment | 122 |
| abstract_inverted_index.experiment. | 60 |
| abstract_inverted_index.recognition | 19, 52 |
| abstract_inverted_index.environment. | 114 |
| abstract_inverted_index.recognition. | 10 |
| abstract_inverted_index.applications. | 129 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |