1,581 research outputs found
A frequency-selective feedback model of auditory efferent suppression and its implications for the recognition of speech in noise
The potential contribution of the peripheral auditory efferent system to our understanding of speech in a background of competing noise was studied using a computer model of the auditory periphery and assessed using an automatic speech recognition system. A previous study had shown that a fixed efferent attenuation applied to all channels of a multi-channel model could improve the recognition of connected digit triplets in noise [G. J. Brown, R. T. Ferry, and R. Meddis, J. Acoust. Soc. Am. 127, 943?954 (2010)]. In the current study an anatomically justified feedback loop was used to automatically regulate separate attenuation values for each auditory channel. This arrangement resulted in a further enhancement of speech recognition over fixed-attenuation conditions. Comparisons between multi-talker babble and pink noise interference conditions suggest that the benefit originates from the model?s ability to modify the amount of suppression in each channel separately according to the spectral shape of the interfering sounds
A computer model of auditory efferent suppression: Implications for the recognition of speech in noise
The neural mechanisms underlying the ability of human listeners to recognize speech in the presence of background noise are still imperfectly understood. However, there is mounting evidence that the medial olivocochlear system plays an important role, via efferents that exert a suppressive effect on the response of the basilar membrane. The current paper presents a computer modeling study that investigates the possible role of this activity on speech intelligibility in noise. A model of auditory efferent processing [ Ferry, R. T., and Meddis, R. (2007). J. Acoust. Soc. Am. 122, 3519?3526 ] is used to provide acoustic features for a statistical automatic speech recognition system, thus allowing the effects of efferent activity on speech intelligibility to be quantified. Performance of the ?basic? model (without efferent activity) on a connected digit recognition task is good when the speech is uncorrupted by noise but falls when noise is present. However, recognition performance is much improved when efferent activity is applied. Furthermore, optimal performance is obtained when the amount of efferent activity is proportional to the noise level. The results obtained are consistent with the suggestion that efferent suppression causes a ?release from adaptation? in the auditory-nerve response to noisy speech, which enhances its intelligibility
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments
We propose a spatial diffuseness feature for deep neural network (DNN)-based
automatic speech recognition to improve recognition accuracy in reverberant and
noisy environments. The feature is computed in real-time from multiple
microphone signals without requiring knowledge or estimation of the direction
of arrival, and represents the relative amount of diffuse noise in each time
and frequency bin. It is shown that using the diffuseness feature as an
additional input to a DNN-based acoustic model leads to a reduced word error
rate for the REVERB challenge corpus, both compared to logmelspec features
extracted from noisy signals, and features enhanced by spectral subtraction.Comment: accepted for ICASSP201
Blind Normalization of Speech From Different Channels
We show how to construct a channel-independent representation of speech that
has propagated through a noisy reverberant channel. This is done by blindly
rescaling the cepstral time series by a non-linear function, with the form of
this scale function being determined by previously encountered cepstra from
that channel. The rescaled form of the time series is an invariant property of
it in the following sense: it is unaffected if the time series is transformed
by any time-independent invertible distortion. Because a linear channel with
stationary noise and impulse response transforms cepstra in this way, the new
technique can be used to remove the channel dependence of a cepstral time
series. In experiments, the method achieved greater channel-independence than
cepstral mean normalization, and it was comparable to the combination of
cepstral mean normalization and spectral subtraction, despite the fact that no
measurements of channel noise or reverberations were required (unlike spectral
subtraction).Comment: 25 pages, 7 figure
Voice input/output capabilities at Perception Technology Corporation
Condensed resumes of key company personnel at the Perception Technology Corporation are presented. The staff possesses recognition, speech synthesis, speaker authentication, and language identification. Hardware and software engineers' capabilities are included
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