King, Simon

Valentini-Botinhao, Cassia

Wester, Mirjam

Yamagishi, Junichi

English

Motivated by the fact that words are not equally confusable, we explore the idea of using word-level intelligibility predictions to selectively boost the harder-to-understand words in a sentence, aiming to improve overall intelligibility in the presence of noise. First, the intelligibility of a set of words from dense and sparse phonetic neighbourhoods was evaluated in isolation. The resulting intelligibility scores were used to inform two sentencelevel experiments. In the first experiment the signal-to-noise ratio of one word was boosted to the detriment of another word. Sentence intelligibility did not generally improve. The intelligibility of words in isolation and in a sentence were found to be significantly different, both in clean and in noisy conditions. For the second experiment, one word was selectively boosted while slightly attenuating all other words in the sentence. This strategy was successful for words that were poorly recognised in that particular context. However, a reliable predictor of word-in-context intelligibility remains elusive, since this involves ? as our results indicate ? semantic, syntactic and acoustic information about the word and the sentence

Edinburgh Research Explorer

A glimpsing model of speech perception in noise,”

A shorttime objective intelligibility measure for time-frequency weighted noisy speech,” in

Analysis of HMMbased lombard speech synthesis,” in

C2H A computational model of H&H-based phonetic contrast in synthetic speech,” in

Can objective measures predict the intelligibility of modiﬁed HMM-based synthetic speech in noise?” in

Continuous control of the degree of articulation in HMM based speech synthesis,” in

Discovering consistent word confusions in noise,” in

Evaluating the intelligibility beneﬁt of speech modiﬁcations in known noise conditions,”

Hearing in the communication society D-2-2 deliverable,”

Improving the understandability of speech synthesis by modeling speech in noise,” in

Mel cepstral coefﬁcient modiﬁcation based on the Glimpse Proportion measure for improving the intelligibility of HMM-generated synthetic speech in noise,” in

Prediction of speech intelligibility based on an auditory preprocessing model,”

Recognizing spoken words: The neighborhood activation model,”

Similarity relations among spoken words: The special status of rimes in

Using neighbourhood density and selective SNR boosting to increase the intelligibility of synthetic speech in noise

https://www.pure.ed.ac.uk/ws/files/14934708/Using_Neighbourhood_Density.pdf

Using neighbourhood density and selective SNR boosting to increase the intelligibility of synthetic speech in noise

Abstract

Similar works

Full text

Available Versions

Edinburgh Research Explorer

Edinburgh Research Explorer