Abstract
We propose a novel computationally efficient real-time microphone array speech enhancement postfilter with a small delay that takes into account features of speech signal and recognition algorithms. The algorithm is efficient for small microphone arrays. The filter is based on applying a binary classification model to the Log Short-Term Spectral Amplitude (Log-STSA). The proposed algorithm allows substantial improvement of recognition accuracy with minor increase in complexity compared to Wiener post-filter and lower complexity compared to existing voice model based approaches. Objective tests using dual microphone array, ETSI binaural noise database, TIDIGITS database, and CMU Sphinx 4 speech recognizer demonstrate overall 41% Error Rate reduction for SNR from 15 dB to 0 dB. Subjective evaluation also demonstrates substantial noise reduction and intelligibility improvement without musical noise artifacts common for Wiener and Spectral Subtraction based methods. Testing with SiSEC10 four microphone linear equispaced array database shows that recognition accuracy is improved with increased base and/or number of microphones in array.
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The work was supported by Saint-Petersburg State University, grants 6.37.349.2015 and 6.38.230.2015.
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Salishev, S., Klotchkov, I., Barabanov, A. (2017). Microphone Array Post-filter in Frequency Domain for Speech Recognition Using Short-Time Log-Spectral Amplitude Estimator and Spectral Harmonic/Noise Classifier. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_52
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DOI: https://doi.org/10.1007/978-3-319-66429-3_52
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