Abstract
The paper proposes an approach for noise robust speech recognition in low resource setting. The approach involves formulation of whole word point process model based on word specific spectral peak event in selected groups of mel banks. The performance of the proposed approach is demonstrated on an isolated word recognizer on noisy speech samples (additive white Gaussian noise) at different SNR levels ranging from 0 dB to clean speech. The training is carried out with examples varying from 5 to 80. Performance comparison with HMM based system trained with mel-frequency cepstral coefficients (MFCC) features show an improvement of 8–17% (absolute) depending on SNR level when the number of training examples are less than 10. Since the approach relies only on positions and magnitudes of spectral peaks derived from spoken word examples without any language specific resources, the same can potentially be applied for any language. It is also shown that our approach recognizes those words better that are poorly recognized by HMMs across all SNR levels.
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Mandal, A., Kumar, K.R.P., Mitra, P. (2017). Point Process Modeling of Spectral Peaks for Low Resource Robust Speech Recognition. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_22
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DOI: https://doi.org/10.1007/978-3-319-71928-3_22
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