Speech Coding Thesis

Speech Coding Thesis-41
To detect the landmarks used in the selective compression strategy, automatic consonant-landmark detection algorithms were developed to handle adverse speech conditions.

In this dissertation we investigate the use of two such embedded noise reduction methods namely, ‘SNR weighting method’ and ‘S-shaped compression’ to improve speech perception in noisy listening conditions.

SNR weighting noise reduction method is an exponential weighting method that uses the instantaneous signal to noise ratio (SNR) estimate to perform noise reduction in each frequency band that corresponds to a particular electrode in the cochlear implant.

Most of the cochlear implant 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 our hypothesis, when listening to speech in fluctuating maskers (e.g., competing talkers), CI users cannot fuse the pieces of the message over temporal gaps because they are unable to perceive reliably the acoustic landmarks introduced by obstruent consonants (e.g., stops).

These landmarks, often blurred in noisy conditions, are evident in spectral discontinuities associated with consonant closures and releases, and are posited to aid listeners in determining word/syllable boundaries.

In the current work we investigate the use of new filter spacing techniques called ‘Semitone filter spacing techniques’ in which filter bandwidths are varied in correspondence to the musical semitone steps.

Noise reduction methods investigated so far for use with cochlear implants are mostly pre-processing methods.

Cochlear implant (CI) user’s performance degrades significantly in noisy environments, especially in non-steady noisy conditions.

Unlike normal hearing listeners, CI users generally perform better when listening to speech in steady-state noise than in fluctuating maskers, and the reasons for that are unclear.


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