195 research outputs found
Bridging the Granularity Gap for Acoustic Modeling
While Transformer has become the de-facto standard for speech, modeling upon
the fine-grained frame-level features remains an open challenge of capturing
long-distance dependencies and distributing the attention weights. We propose
\textit{Progressive Down-Sampling} (PDS) which gradually compresses the
acoustic features into coarser-grained units containing more complete semantic
information, like text-level representation. In addition, we develop a
representation fusion method to alleviate information loss that occurs
inevitably during high compression. In this way, we compress the acoustic
features into 1/32 of the initial length while achieving better or comparable
performances on the speech recognition task. And as a bonus, it yields
inference speedups ranging from 1.20 to 1.47. By reducing the
modeling burden, we also achieve competitive results when training on the more
challenging speech translation task.Comment: ACL 2023 Finding
GFM-Voc: A real-time voice quality modification system
International audienc
An investigation of speaker independent phrase break models in End-to-End TTS systems
This paper presents our work on phrase break prediction in the context of
end-to-end TTS systems, motivated by the following questions: (i) Is there any
utility in incorporating an explicit phrasing model in an end-to-end TTS
system?, and (ii) How do you evaluate the effectiveness of a phrasing model in
an end-to-end TTS system? In particular, the utility and effectiveness of
phrase break prediction models are evaluated in in the context of childrens
story synthesis, using listener comprehension. We show by means of perceptual
listening evaluations that there is a clear preference for stories synthesized
after predicting the location of phrase breaks using a trained phrasing model,
over stories directly synthesized without predicting the location of phrase
breaks.Comment: Submitted for review to IEEE Acces
Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome
Fragile X syndrome (FXS) and Rett syndrome (RTT) are developmental disorders currently not diagnosed before toddlerhood. Even though speech-language deficits are among the key symptoms of both conditions, little is known about infant vocalisation acoustics for an automatic earlier identification of affected individuals. To bridge this gap, we applied intelligent audio analysis methodology to a compact dataset of 4454 home-recorded vocalisations of 3 individuals with FXS and 3 individuals with RTT aged 6 to 11 months, as well as 6 age- and gender-matched typically developing controls (TD). On the basis of a standardised set of 88 acoustic features, we trained linear kernel support vector machines to evaluate the feasibility of automatic classification of (a) FXS vs TD, (b) RTT vs TD, (c) atypical development (FXS+RTT) vs TD, and (d) FXS vs RTT vs TD. In paradigms (a)–(c), all infants were correctly classified; in paradigm (d), 9 of 12 were so. Spectral/cepstral and energy-related features were most relevant for classification across all paradigms. Despite the small sample size, this study reveals new insights into early vocalisation characteristics in FXS and RTT, and provides technical underpinnings for a future earlier identification of affected individuals, enabling earlier intervention and family counselling
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