- B. Raj, R. Stern. 2009. Missing-Feature Approaches in Speech Recognition [ Improving recognition accuracy in noise by using partial spectrographic information ]. Abstract: D espite decades of focused research on the problem, the accuracy of automatic speech recognition (ASR) systems is still adversely affected by noise and other sources of acoustical variability. For example, there are presently dozens, if not hundreds, of algorithms that have been developed to cope with the effects of quasistationary additive noise and linear filtering of the channel [16], [23]. While these approaches are reasonably effective in the context of their intended purposes, they are generally ineffective in improving recognition accuracy in many more difficult environments. Some of these more challenging conditions, which are frequently encountered in some of the most important potential speech recognition applications today, include speech in the presence of transient and nonstationary disturbances (as in many factory, military, and telematic environments), speech in the presence of background music or background speech (as in the automatic transcription of broadcast program material as well as in many natural environments), and speech at very low signal-to-noise ratios (SNRs). Conventional environmental compensation provides only limited benefit for these problems even today. For example, Raj et al. [29] showed that while codeword-dependent cepstral normalization is highly effective in dramatically reducing the impact of additive broadband noise on speech recognition accuracy, it is relatively ineffective when speech is presented to the system in the presence of background music, for reasons that are believed to be a consequence of the nonstationary nature of the acoustical degradation. [Bhiksha Raj and Richard M. Stern]
