By Alejandro Acero (auth.)

ISBN-10: 1461363667

ISBN-13: 9781461363668

ISBN-10: 1461531225

ISBN-13: 9781461531227

The desire for automated speech popularity platforms to be powerful with appreciate to alterations of their acoustical atmosphere has develop into extra largely favored lately, as extra platforms are discovering their method into functional functions. even supposing the problem of environmental robustness has bought just a small fraction of the eye dedicated to speaker independence, even speech acceptance platforms which are designed to be speaker self reliant often practice very poorly once they are established utilizing a special form of microphone or acoustical atmosphere from the only with which they have been expert. using microphones except a "close­ conversing" headset additionally has a tendency to seriously degrade speech acceptance -performance. Even in quite quiet workplace environments, speech is degraded via additive noise from fanatics, slamming doorways, and different conversations, in addition to by means of the results of unknown linear filtering bobbing 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 traces, or purposes deployed in additional antagonistic acoustical environments akin to motorized vehicles, manufacturing facility flooring, oroutdoors call for some distance greaterdegrees ofenvironmental robustness. There are a number of alternative ways of establishing acoustical robustness into speech attractiveness platforms. Arrays of microphones can be utilized to enhance a directionally-sensitive procedure 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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Extra resources for Acoustical and Environmental Robustness in Automatic Speech Recognition

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It has been applied in the following contexts: • Speaker Verification. Atal [3] was the first author to propose some kind of long-term normalization for a speaker verification system. The average of the cepstrum vector throughout the utterance was subtracted from each individual vector in an attempt to reduce the possibility that an imposter could be misclassified. Furui [24] confirmed in another speakerrecognition experiment that subtracting a long-term average maintained a good recognition rate while providing robustness against channels with different frequency responses.

Lippmann et al. [53] pooled all the data from speakers speaking under different styles (fast, soft, angry, loud, Lombard 17 ) to increase the robustness of the recognition system to different speech styles. Lee and Hon [47] showed that 17When the speaker is in an especially noisy environmcnt. the produced speech. called Lombard speech, exhibits different charactcristics than normal specch. This effcct can also be simulated by recording speech from talkers in a quiet environment while they are listening to high-level noise presented through headphones.

Using the census database, the more compact Sphinx system, and DEC 3100 tSperplexiry is an information theoretic measure of the amount of constraint imposed by a finite-state grammar. If no grammar is used. the perplexity coincides with the size of the vocabulary. If a grammar is used to restrain the search space. the perplexity will be lower than the size of the vocabulary. In general higher perplexity tasks produce higher etTor rates. 24 ACOUSTICAl. AND ENVIRONMENTAL RODUSTNESS workstation, we were able to reduce the training time to the point that an entire train-and-test cycle could be performed in about 10 hours.

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Acoustical and Environmental Robustness in Automatic Speech Recognition by Alejandro Acero (auth.)

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