By Alejandro Acero (auth.)
The desire for computerized speech popularity platforms to be powerful with appreciate to adjustments of their acoustical atmosphere has develop into extra largely liked lately, as extra structures are discovering their means into sensible functions. even though the difficulty of environmental robustness has bought just a small fraction of the eye dedicated to speaker independence, even speech attractiveness platforms which are designed to be speaker autonomous often practice very poorly once they are verified utilizing a distinct kind of microphone or acoustical surroundings from the only with which they have been informed. using microphones except a "close speaking" headset additionally has a tendency to critically degrade speech reputation -performance. Even in fairly quiet workplace environments, speech is degraded by way of additive noise from fanatics, slamming doorways, and different conversations, in addition to by way of the consequences of unknown linear filtering coming up reverberation from floor reflections in a room, or spectral shaping via microphones or the vocal tracts of person audio system. Speech-recognition platforms designed for long-distance mobile strains, or purposes deployed in additional adversarial acoustical environments resembling motorcars, manufacturing unit flooring, oroutdoors call for some distance greaterdegrees ofenvironmental robustness. There are a number of other ways of establishing acoustical robustness into speech acceptance structures. Arrays of microphones can be utilized to increase a directionally-sensitive method that resists intelference from competing talkers and different noise assets which are spatially separated from the resource of the specified speech signal.
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Additional resources for Acoustical and Environmental Robustness in Automatic Speech Recognition
The Environment The census database was recorded simultaneously ill stereo using both the Sennheiser HMD224 close-talking microphone that has been a standard in previous DARPA evaluations, and a desk-top Crown PZM6fs microphone. The recordings were made in one of the eMU speech laboratories (the "Agora" lab), which has high ceilings, concrete-block walls, and a carpeted floor. Although the recordings were made behind an acoustic partition, no attempt was made to silence other users of the room during recording sessions, and there is consequently a significant amount of audible interference from other talkers, key clicks from other workstations, slamming doors, and other sources of interference, as well as the reverberation from the room itself.
23], Silverman , Van Compernolle ). The goal of this approach is to develop a directivity pattern so that 12 ACOUSTICAL AND ENVIRONMENTAL ROI3USTNESS noise sources arriving from a different angle than the desired speech are attenuated. While microphone arrays need to be explored in the future, we believe that there is still room for improvement with a monophonic signal. In addition, these two approaches can complement each other. While most previous efforts are geared towards suppressing stationary noise, some authors have focused on the difficult problem of speaker separation (Min et al.
We also did not address the issue of non-stationary noise either because we consider it an extremely difficult problem. In this monograph we will primarily explore approaches based on short-time spectral amplitude estimation and techniques that use mixture densities. They offer an attractive compromise between efficiency and accuracy. 3. Towards Environment-Independent Recognition The goal of our research is to increase the robustness of the speech recognition systems with respect to changes in the environment.
Acoustical and Environmental Robustness in Automatic Speech Recognition by Alejandro Acero (auth.)