Adding controlled amount of noise to improve recognition of compressed and spectrally distorted speech

This paper deals with the recognition of speech whose spectrum is notably distorted by lossy compression (namely MP3) or by some implementations of `speech enhancement´ techniques. We show that these non-linear treatments can introduce gaps in spectrum that significantly change the distribution of MFCCs and degrade performance of ASR. We propose a method that measures the level of spectrum distortion and use it for adding a controlled amount of noise to the signal. It effectively masks the gaps and helps namely in situations where the source and parameters of the distortion are not known and hence we cannot use a properly matched acoustic model. In spite of its simplicity, the method can improve significantly speech recognition of highly compressed or spectrally distorted signals. We demonstrate it in several large experiments conducted on publicly available speech databases, in two languages and for two types of spectral distortion.