Auditory Model-Based Design and Optimization of Feature Vectors for Automatic Speech Recognition
2011 (English)In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, Vol. 19, no 6, 1813-1825 p.Article in journal (Refereed) Published
Using spectral and spectro-temporal auditory models along with perturbation-based analysis, we develop a new framework to optimize a feature vector such that it emulates the behavior of the human auditory system. The optimization is carried out in an offline manner based on the conjecture that the local geometries of the feature vector domain and the perceptual auditory domain should be similar. Using this principle along with a static spectral auditory model, we modify and optimize the static spectral mel frequency cepstral coefficients (MFCCs) without considering any feedback from the speech recognition system. We then extend the work to include spectro-temporal auditory properties into designing a new dynamic spectro-temporal feature vector. Using a spectro-temporal auditory model, we design and optimize the dynamic feature vector to incorporate the behavior of human auditory response across time and frequency. We show that a significant improvement in automatic speech recognition (ASR) performance is obtained for any environmental condition, clean as well as noisy.
Place, publisher, year, edition, pages
2011. Vol. 19, no 6, 1813-1825 p.
Auditory models, mel frequency cepstral coefficient (MFCC), speech recognition
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-38976DOI: 10.1109/TASL.2010.2101597ISI: 000293702300030OAI: oai:DiVA.org:kth-38976DiVA: diva2:438758
FunderICT - The Next Generation