Coupling acoustic and language models in speech recognition in noisy conditions
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School of Science |
Master's thesis
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Date
2012
Department
Major/Subject
Informaatiotekniikka
Mcode
T-61
Degree programme
Language
en
Pages
[7] + 45
Series
Abstract
Automatic speech recognition systems have difficulties with adapting to different speakers and acoustic environments, unlike humans that can overcome them without great difficulties. This represents an obstacle in developing real world applications where the speech recognition system is used in varying noisy environments. This work addresses the problem of automatic speech recognition in noisy conditions, focusing on coupling acoustic and language models to achieve better speech recognition results. Specifically, the letter error rates and word error rates are compared for different coupling values of the acoustic and language models in the same data set with different values of SNR artificially added noise. Results of these experiments show a near linear relationship between SNR in dB and the factor that adjusts language model logarithmic likelihood. An approach based on spectral subtraction is presented to estimate the optimal scaling factor based on mapping an estimation of the SNR to the language model scale. The results obtained show better recognition rates than the baseline fixed o to a constant LM scale value. Performance near to optimal LM scale on average for each SNR is achieved by the given approach. However, the results are still far from the ones obtained from the best utterance wise LM scale.Description
Supervisor
Kurimo, MikkoThesis advisor
Palomäki, KalleKallasjoki, Heikki
Keywords
automatic speech recognition, acoustic modelling, language modelling, SNR estimation, spectral substraction