Simple and artefact-free spectral modifications for enhancing the intelligibility of casual speech

Koutsogiannaki, Maria; Stylianou, Yannis

doi:10.1109/icassp.2014.6854483

Cited by 3 publications

(1 citation statement)

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“…However, our previous mapping experiments only showed very modest improvements, and were conducted only on vowels [14]. More recently, a mixed-filtering approach [15], which isolated, then boosted important frequency regions in habitual speech, resulted in an objective (but not subjective) improvement of speech intelligibility and a subjective improvement of speech quality.…”

Section: Introductionmentioning

confidence: 99%

Using a Manifold Vocoder for Spectral Voice and Style Conversion

Dinh

Kain²,

Tjaden

2019

Interspeech 2019

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We propose a new type of spectral feature that is both compact and interpolable, and thus ideally suited for regression approaches that involve averaging. The feature is realized by means of a speaker-independent variational autoencoder (VAE), which learns a latent space based on the low-dimensional manifold of high-resolution speech spectra. In vocoding experiments, we showed that using a 12-dimensional VAE feature (VAE-12) resulted in significantly better perceived speech quality compared to a 12-dimensional MCEP feature. In voice conversion experiments, using VAE-12 resulted in significantly better perceived speech quality as compared to 40-dimensional MCEPs, with similar speaker accuracy. In habitual to clear style conversion experiments, we significantly improved the speech intelligibility for one of three speakers, using a custom skipconnection deep neural network, with the average keyword recall accuracy increasing from 24% to 46%.

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Section: Introductionmentioning

confidence: 99%