Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition
نویسندگان
چکیده
منابع مشابه
Likelihood-Maximizing-Based Multiband Spectral Subtraction for Robust Speech Recognition
Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech...
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Spectral Subtraction (SS), as a speech enhancement technique, originally designed for improving quality of speech signal judged by human listeners. it usually improve the quality and intelligibility of speech signals, while the speech recognition systems need compensation techniques capable of reducing the mismatch between the noisy speech features and the clean models. This paper proposes a no...
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Spectral subtraction (SS) is derived using maximum likelihood estimation assuming both noise and speech follow Gaussian distributions and are independent from each other. Under this assumption, noisy speech, speech contaminated by noise, also follows a Gaussian distribution. However, it is well known that noisy speech observed in real situations often follows a heavytailed distribution, not a G...
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The weakness of conventional spectral subtractive-type algorithm is identified and presented in Section 2. The proposed remedial approach is described in Section 3. In Section 4, we show the proposed method’s effectiveness over conventional methods with representative experiments using Aurora 2. Concluding remarks are provided in Section 5. This paper addresses a novel noise-compensation scheme...
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ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2009
ISSN: 1687-6180
DOI: 10.1155/2009/878105