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

By Alejandro Acero (auth.)

The desire for automated speech popularity structures to be powerful with appreciate to adjustments of their acoustical atmosphere has develop into extra commonly favored lately, as extra structures are discovering their approach into useful purposes. even supposing 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 sustaining usually practice very poorly once they are proven utilizing a unique kind of microphone or acoustical atmosphere from the single with which they have been knowledgeable. using microphones except a "close­ speaking" headset additionally has a tendency to seriously degrade speech reputation -performance. Even in particularly quiet place of work environments, speech is degraded by means of additive noise from enthusiasts, slamming doorways, and different conversations, in addition to via the results of unknown linear filtering coming up reverberation from floor reflections in a room, or spectral shaping by way of microphones or the vocal tracts of person audio system. Speech-recognition structures designed for long-distance mobile traces, or functions deployed in additional hostile acoustical environments reminiscent of motorcars, manufacturing unit flooring, oroutdoors call for a long way greaterdegrees ofenvironmental robustness. There are numerous other ways of creating acoustical robustness into speech reputation structures. Arrays of microphones can be utilized to advance 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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For this we use the concept of a universal acoustic space that is transformed to match the acoustic space of the current environment. We show that a few 14 ACOUSTICAL AND ENVmONMENTAL ROBUSTNESS seconds of speech are sufficient to adapt to a different acoustical environment. 2. Processing in the Cepstral Domain: A Unified View While successful noise-suppression algorithms operate in the frequency domain, most successful continuous speech recognizers operate in the cepstral domain. In this monograph we will describe algorithms that perform the noise suppression in the cepstral domain, so that a larger degree of integration can be achieved for cepstral-based systems.

The separation of the two curves in each panel provides an indication of signal-to-noise ratio for each microphone as a function of frequency. It can also be seen that the Crown PZM6sf produces greater spectral tilt. 5. Discussion of SNR Measures In summary, the characterization of a speech database should not depend on aspects of the speech signal that are irrelevant such as • Silent Periods and Background Noise. The objective measurement should reflect the quality of the recording, which does not depend on the length of the silent periods or background noise.

52], which is an approximate maximum-likelihood method. 3. Hidden Markov Models Hidden Markov Models (HMM), the dominant technology in continuous speech recognition, constitutes the recognition engine used in SPHINX. Rabiner and Juang [72] present a good review of HMMs, Picone [69] offers a summary of HMM applications to speech recognition. Briefly, an HMM is a collection of states connected by transitions. Each transition carries two sets of probabilities: • A transition probability which provides the probability for taking a transition from one state to the next, and • An output probability density function (pdf), which defines the conditional probability of emitting each output symbol from a finite alphabet given that that transition is taken.

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