AM-demodulation of speech spectra and its application io noise robust speech recognition
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Summary
A novel algorithm that resembles amplitude demodulation in the frequency domain is introduced, and its application to automatic speech recognition (ASR) is studied and it is shown that this technique achieves superior performance to MFCCs in the presence of additive noise.
- Type
- article
- Published
- 2000-10-16
- Cited by
- 12
- References
- 7
- OpenAlex
- https://openalex.org/W95495579
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:13940524
Keywords
Demodulation, Computer science, Speech recognition, Noise (video), Harmonic
References
- Acoustic Theory Of Speech Production
- Speech analysis and synthesis using an AM-FM modulation model
- The modulation spectrogram: in pursuit of an invariant representation of speech
- A model of dynamic auditory perception and its application to robust word recognition
- On properties of modulation spectrum for robust automatic speech recognition
- Communication Systems
- The Acoustic Theory of Speech Production
- Communication Systems
Cited by
- Noise robust feature extraction for ASR using the Aurora 2 database
- Evaluation of noise robust features on the Aurora databases
- Split-band perceptual harmonic cepstral coefficients as acoustic features for speech recognition
- Non-linear feature extraction for robust speech recognition in stationary and non-stationary noise
- Perceptual harmonic cepstral coefficients for speech recognition in noisy environment
- Fepstrum representation of speech signal
- Fepstrum and Carrier Signal Decomposition of Speech Signals Through Homomorphic Filtering
- On factorizing spectral dynamics for robust speech recognition
- Environmental and Speaker Robustness in Automatic Speech Recognition with Limited Learning Data
- A pitch-based spectral enhancement technique for robust speech processing
- Modulation and performance of synchronous demodulation for speech signal detection and dialect intelligibility
- Wavelet Based Noise Robust Features for Speaker Recognition
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