322 research outputs found
Towards Personalized Synthesized Voices for Individuals with Vocal Disabilities: Voice Banking and Reconstruction
When individuals lose the ability to produce their own speech, due to degenerative diseases such as motor neurone disease (MND) or Parkinson’s, they lose not only a functional means of communication but also a display of their individual and group identity. In order to build personalized synthetic voices, attempts have been made to capture the voice before it is lost, using a process known as voice banking. But, for some patients, the speech deterioration frequently coincides or quickly follows diagnosis. Using HMM-based speech synthesis, it is now possible to build personalized synthetic voices with minimal data recordings and even disordered speech. The power of this approach is that it is possible to use the patient’s recordings to adapt existing voice models pre-trained on many speakers. When the speech has begun to deteriorate, the adapted voice model can be further modified in order to compensate for the disordered characteristics found in the patient’s speech. The University of Edinburgh has initiated a project for voice banking and reconstruction based on this speech synthesis technology. At the current stage of the project, more than fifteen patients with MND have already been recorded and five of them have been delivered a reconstructed voice. In this paper, we present an overview of the project as well as subjective assessments of the reconstructed voices and feedback from patients and their families
Analysis of Speaker Clustering Strategies for HMM-Based Speech Synthesis
This paper describes a method for speaker clustering, with the application of building average voice models for speakeradaptive HMM-based speech synthesis that are a good basis for adapting to specific target speakers. Our main hypothesis is that using perceptually similar speakers to build the average voice model will be better than use unselected speakers, even if the amount of data available from perceptually similar speakers is smaller. We measure the perceived similarities among a group of 30 female speakers in a listening test and then apply multiple linear regression to automatically predict these listener judgements of speaker similarity and thus to identify similar speakers automatically. We then compare a variety of average voice models trained on either speakers who were perceptually judged to be similar to the target speaker, or speakers selected by the multiple linear regression, or a large global set of unselected speakers. We find that the average voice model trained on perceptually similar speakers provides better performance than the global model, even though the latter is trained on more data, confirming our main hypothesis. However, the average voice model using speakers selected automatically by the multiple linear regression does not reach the same level of performance. Index Terms: Statistical parametric speech synthesis, hidden Markov models, speaker adaptatio
The DeepZen Speech Synthesis System for Blizzard Challenge 2023
This paper describes the DeepZen text to speech (TTS) system for Blizzard
Challenge 2023. The goal of this challenge is to synthesise natural and
high-quality speech in French, from a large monospeaker dataset (hub task) and
from a smaller dataset by speaker adaptation (spoke task). We participated to
both tasks with the same model architecture. Our approach has been to use an
auto-regressive model, which retains an advantage for generating natural
sounding speech but to improve prosodic control in several ways. Similarly to
non-attentive Tacotron, the model uses a duration predictor and gaussian
upsampling at inference, but with a simpler unsupervised training. We also
model the speaking style at both sentence and word levels by extracting global
and local style tokens from the reference speech. At inference, the global and
local style tokens are predicted from a BERT model run on text. This BERT model
is also used to predict specific pronunciation features like schwa elision and
optional liaisons. Finally, a modified version of HifiGAN trained on a large
public dataset and fine-tuned on the target voices is used to generate speech
waveform. Our team is identified as O in the the Blizzard evaluation and MUSHRA
test results show that our system performs second ex aequo in both hub task
(median score of 0.75) and spoke task (median score of 0.68), over 18 and 14
participants, respectively.Comment: Blizzard Challenge 202
Feeling, Knowledge, Self-Preservation: Audre Lorde’s Oppositional Agency and Some Implications for Ethics
Throughout her work, Audre Lorde maintains that her self-preservation in the face of oppression depends on acting from the recognition and valorization of her feelings as a deep source of knowledge. This claim, taken as a portrayal of agency, poses challenges to standard positions in ethics, epistemology, and moral psychology. This article examines the oppositional agency articulated by Lorde’s thought, locating feeling, poetry, and the power she calls “the erotic” within her avowed project of self-preservation. It then explores the implications of taking seriously Lorde’s account, particularly for theorists examining ethics and epistemology under nonideal social conditions. For situations of sexual intimacy, for example, Lorde’s account unsettles prevailing assumptions about the role of consent in responsibility between sexual partners. I argue that obligations to solicit consent and respect refusal are not sufficient to acknowledge the value of agency in intimate encounters when agency is oppositional in the way Lorde describes
A Project Based Approach to Statistics and Data Science
In an increasingly data-driven world, facility with statistics is more
important than ever for our students. At institutions without a statistician,
it often falls to the mathematics faculty to teach statistics courses. This
paper presents a model that a mathematician asked to teach statistics can
follow. This model entails connecting with faculty from numerous departments on
campus to develop a list of topics, building a repository of real-world
datasets from these faculty, and creating projects where students interface
with these datasets to write lab reports aimed at consumers of statistics in
other disciplines. The end result is students who are well prepared for
interdisciplinary research, who are accustomed to coping with the
idiosyncrasies of real data, and who have sharpened their technical writing and
speaking skills
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