By Alejandro Acero (auth.)
The want for computerized speech attractiveness structures to be strong with admire to alterations of their acoustical surroundings has develop into extra largely preferred in recent times, as extra structures are discovering their means into functional purposes. even though the difficulty of environmental robustness has obtained just a small fraction of the eye dedicated to speaker independence, even speech acceptance structures which are designed to be speaker self sufficient usually practice very poorly after they are proven utilizing a unique kind of microphone or acoustical surroundings from the single with which they have been expert. using microphones except a "close conversing" headset additionally has a tendency to critically degrade speech attractiveness -performance. Even in rather quiet workplace environments, speech is degraded via additive noise from lovers, slamming doorways, and different conversations, in addition to through the results of unknown linear filtering coming up reverberation from floor reflections in a room, or spectral shaping through microphones or the vocal tracts of person audio system. Speech-recognition structures designed for long-distance cellphone strains, or purposes deployed in additional antagonistic acoustical environments equivalent to motorized vehicles, manufacturing unit flooring, oroutdoors call for some distance greaterdegrees ofenvironmental robustness. There are numerous other ways of establishing acoustical robustness into speech reputation structures. Arrays of microphones can be utilized to improve a directionally-sensitive method that resists intelference from competing talkers and different noise resources which are spatially separated from the resource of the specified speech signal.
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Additional info for Acoustical and Environmental Robustness in Automatic Speech Recognition
Since speech recognition systems don't use the phase information, we will not be concerned with that issue here. The noise spectrum was estimated by averaging the magnitude of several noise frames. At low SNR the processed speech exhibited a residual noise, characterized by random spikes. He proposed additional residual noise reduction schemes, especially during non-speech activity. Berouti et al. 4) in which the amount of noise subtraction depended on the SNR of the particular frame. The enhanced speech obtained by these methods exhibited a greater SNR, although this processing didn't increase the intelligibility.
We show in this monograph that conditioning on the instantaneous SNR 13 is advantageous. We show that the instantaneous SNR captures a great deal of the information needed to perform the compensation. Although frequency normalization may appear to be outside the scope of the problems of robustness to changes in the environment, we show that using the processing in the cepstral domain together with the concept of normalization of the acoustic space allow us to perform some adaptation to the long-term characteristics of the speaker.
Nevertheless, we have also noted that spectral tilt can affect the performance of speech-recognition systems as well. In this monograph we develop a set of algorithms to accomplish the joint normalization of noise and spectral tilt. g. Van Compemolle (11), Erell and Weintraub ), both phenomena were treated independently. We will show that there is indeed an interaction between these phenomena and that further benefit is obtained if joint normalization is performed. We will present several algorithms that adapt to new acoustical environments by estimating the noise and spectral tilt from input data.