logo Idiap Research Institute        
 [BibTeX] [Marc21]
Integrating Articulatory Features using Kullback-Leibler Divergence based Acoustic Model for Phoneme Recognition
Type of publication: Idiap-RR
Citation: Rasipuram_Idiap-RR-02-2011
Number: Idiap-RR-02-2011
Year: 2011
Month: 2
Institution: Idiap
Abstract: In this paper, we propose a novel framework to integrate articulatory features (AFs) into HMM- based ASR system. This is achieved by using posterior probabilities of different AFs (estimated by multilayer perceptrons) directly as observation features in Kullback-Leibler divergence based HMM (KL-HMM) system. On the TIMIT phoneme recognition task, the proposed framework yields a phoneme recognition accuracy of 72.4% which is comparable to KL-HMM system using posterior probabilities of phonemes as features (72.7%). Furthermore, a best performance of 73.5% phoneme recognition accuracy is achieved by jointly modelling AF probabilities and phoneme probabilities as features. This shows the efficacy and flexibility of the proposed approach.
Keywords: articulatory features, Automatic Speech Recognition, Kullback-Leibler divergence based hidden Markov model, multilayer perceptron, phonemes, posterior probabilities
Projects Idiap
Authors Rasipuram, Ramya
Magimai.-Doss, Mathew
Crossref by Rasipuram_ICASSP_2011
Added by: [ADM]
Total mark: 0
  • Rasipuram_Idiap-RR-02-2011.pdf (MD5: 169a1d3c3735fa78672449f76e04adf5)