Separating the contributions of hearing, lexical knowledge and speech production to speech perception scores in children with hearing impairments.

Open-set word and sentence speech-perception test scores are commonly used as a measure of hearing abilities in children and adults using cochlear implants and/or hearing aids. These tests are usually presented auditorily with a verbal response. In the case of children, scores are typically lower and more variable than for adults with hearing impairments using similar devices. It is difficult to interpret children's speech-perception scores without considering the effects of lexical knowledge and speech-production abilities on their responses. This study postulated a simple mathematical model to describe the effects of hearing, lexical knowledge, and speech production on the perception test scores for monosyllabic words by children with impaired hearing. Thirty-three primary-school children with impaired hearing, fitted with hearing aids and/or cochlear implants, were evaluated using speech-perception, reading-aloud, speech-production, and language measures. These various measures were incorporated in the mathematical model, which revealed that performance in an open-set word-perception test in the auditory-alone mode is strongly dependent on residual hearing levels, lexical knowledge, and speech-production abilities. Further applications of the model provided an estimate of the effect of each component on the overall speech-perception score for each child.

Evaluating parts-of-speech taggers for use in a text-to-scene conversion system

This paper presents parts-of-speech tagging as a first step towards an autonomous text-to-scene conversion system. It categorizes some freely available taggers, according to the techniques used by each in order to automatically identify word-classes. In addition, the performance of each identified tagger is verified experimentally. The SUSANNE corpus is used for testing and reveals the complexity of working with different tagsets, resulting in substantially lower accuracies in our tests than in those reported by the developers of each tagger. The taggers are then grouped to form a voting system to attempt to raise accuracies, but in no cases do the combined results improve upon the individual accuracies. Additionally a new metric, agreement, is tentatively proposed as an indication of confidence in the output of a group of taggers where such output cannot be validated.

Adaptive speech enhancement with varying noise backgrounds

We present a new approach for speech enhancement in the presence of non-stationary and rapidly changing background noise. A distributed microphone system is used to capture the acoustic characteristics of the environment. The input of each microphone is then classified either as speech or one of the predetermined noise types. Further enhancement of speech in respective microphones is carried out using a modified spectral subtraction algorithm that incorporates multiple noise models to quickly adapt to rapid background noise changes. Tests on real world speech captured under diverse conditions demonstrate the effectiveness of this method.