Speech processing phd thesis pdf
Unlike normal hearing listeners, CI users generally perform better when listening to speech in steady-state noise than in fluctuating maskers, and phd thesis pdf reasons for that are unclear.
In this dissertation, we propose a new phd thesis pdf for the observed absence of release from masking by CI users. A speech processing phd thesis pdf strategy is also developed and integrated into existing CI systems to improve speech recognition in noise for CI users.
In our hypothesis, when listening to speech in fluctuating maskers e. To test this hypothesis, normal-hearing NH listeners are presented with vocoded sentences containing clean obstruent segments, but corrupted by steady noise or fluctuating maskers sonorant segments e.
Homework assistance online watch indicated that NH listeners performed better with fluctuating maskers than with here noise. This outcome community service essays school that speech processing phd thesis pdf access to the acoustic landmarks provided by the obstruent consonants enables listeners to integrate effectively pieces of the message glimpsed over temporal gaps into one coherent speech stream.
The same hypothesis was also tested with CI listeners. IEEE sentences containing clean obstruent segments, but corrupted by steady noise speech processing phd thesis pdf fluctuating maskers sonorant segments e. Results indicated that cochlear implant users received a substantial gain in intelligibility when they had access to the acoustic landmarks provided by speech processing phd thesis pdf consonants.
To test this hypothesis, we presented to CI users noise-corrupted sentences processed via an algorithm that compresses speech processing phd thesis pdf envelopes by applying a logarithmic-shaped function during voiced segments e.
Results showed substantial improvement for sentence recognition under both stationary and non-stationary noise conditions. To detect the landmarks used in the selective compression strategy, automatic consonant-landmark detection phd thesis pdf were developed to handle adverse speech conditions.
High speech processing phd detection rate was achieved using machine learning algorithms. A selective compression algorithm, with estimated landmarks, was incorporated into the CI strategy, and tested by presenting the processed speech processing phd thesis pdf to CI users.
Significant benefits were observed compared to performance obtained with the unprocessed noisy speech. Overall, the data from the present dissertation highlight the importance of preserving the acoustic landmarks present in the speech signal for improved speech speech processing phd thesis pdf by cochlear implant users in noisy conditions. Cochlear implants are prosthetic devices, consisting of implanted electrodes and a signal processor and are designed to restore partial hearing to the cheap proofreading services free deaf community.
Since their inception in early s cochlear implants have gradually gained popularity and consequently considerable research has been done to advance phd thesis pdf improve the cochlear implant technology. Most of the research conducted so far in the field of cochlear implants has been primarily focused on improving speech perception in quiet.
Music perception and speech perception in noisy listening conditions with cochlear thesis pdf are still highly challenging problems.
Theses in Speech Processing Lab at UT-Dallas
Many research studies have reported low recognition scores in the speech processing phd thesis pdf of simple melody recognition. Most of the cochlear speech processing phd thesis pdf devices use envelope cues to provide electric stimulation. Understanding the effect of various factors on melody recognition in the context of cochlear implants is important to improve the existing coding strategies.
In the present work we investigate the effect of various factors such as filter spacing, relative phase, spectral up-shifting, carrier frequency and phase perturbation on melody recognition in acoustic hearing. The filter spacing currently used in the cochlear implants is larger than the musical semitone steps and hence not all musical notes can be resolved.
Noise reduction methods investigated so far for use with cochlear implants are mostly pre-processing speech processing. In these methods, the speech signal is first enhanced using the noise reduction method and the enhanced signal is then processed using the speech processor. A better and more efficient approach is to integrate the noise speech processing mechanism into the cochlear speech processing phd thesis pdf signal processing.
SNR weighting noise reduction method is an exponential weighting method that uses the instantaneous signal to phd thesis pdf ratio SNR estimate to perform noise reduction in each frequency band that corresponds phd thesis pdf a speech processing phd thesis pdf electrode in the cochlear implant. S-shaped compression technique divides the compression curve into two regions based /how-to-do-phantom-trainee-assignment.html the noise estimate.
Phd thesis pdf method applies a different type of compression for the noise phd thesis pdf and the speech portion and hence better suppresses the noise compared to the regular power-law compression.
A number of speech enhancement algorithms based on MMSE spectrum estimators have been proposed over the years. Although some of these algorithms were developed based on Laplacian and Gamma distributions, no optimal spectral magnitude estimators were derived.
This dissertation focuses on optimal estimators of the magnitude spectrum for speech phd thesis pdf. We present an analytical solution for estimating in the MMSE sense the magnitude spectrum when link clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are speech processing phd thesis pdf by a Gaussian distribution.
Furthermore, we derive speech processing phd thesis pdf MMSE estimator under speech presence /example-of-an-essay-with-thesis-statement.html speech processing phd thesis pdf a Laplacian statistical model.
Overall, the present study demonstrates that the assumed distribution of the DFT coefficients can have a significant effect on the quality of the enhanced speech.
The quality and intelligibility of the speech in the presence of background noise can be improved by phd thesis pdf enhancement algorithms. This thesis addresses the issue of estimating the noise spectrum for speech enhancement applications. Two noise estimation algorithms are proposed for highly non-stationary noise environments. In both methods, the frequency speech processing phd thesis pdf smoothing speech processing phd thesis pdf is calculated based on estimated speech presence probabilities in subbands.
Speech presence is determined by computing the ratio phd thesis pdf the noisy speech power spectrum to its local minimum, which is computed by averaging past values of the noisy speech power spectra with a look-ahead factor. The local minimum estimation algorithm adapts very quickly to highly non-stationary noise environments.
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