459 research outputs found
On the Impact of Voice Anonymization on Speech-Based COVID-19 Detection
With advances seen in deep learning, voice-based applications are burgeoning,
ranging from personal assistants, affective computing, to remote disease
diagnostics. As the voice contains both linguistic and paralinguistic
information (e.g., vocal pitch, intonation, speech rate, loudness), there is
growing interest in voice anonymization to preserve speaker privacy and
identity. Voice privacy challenges have emerged over the last few years and
focus has been placed on removing speaker identity while keeping linguistic
content intact. For affective computing and disease monitoring applications,
however, the paralinguistic content may be more critical. Unfortunately, the
effects that anonymization may have on these systems are still largely unknown.
In this paper, we fill this gap and focus on one particular health monitoring
application: speech-based COVID-19 diagnosis. We test two popular anonymization
methods and their impact on five different state-of-the-art COVID-19 diagnostic
systems using three public datasets. We validate the effectiveness of the
anonymization methods, compare their computational complexity, and quantify the
impact across different testing scenarios for both within- and across-dataset
conditions. Lastly, we show the benefits of anonymization as a data
augmentation tool to help recover some of the COVID-19 diagnostic accuracy loss
seen with anonymized data.Comment: 11 pages, 10 figure
Disentangling Prosody Representations with Unsupervised Speech Reconstruction
Human speech can be characterized by different components, including semantic
content, speaker identity and prosodic information. Significant progress has
been made in disentangling representations for semantic content and speaker
identity in Automatic Speech Recognition (ASR) and speaker verification tasks
respectively. However, it is still an open challenging research question to
extract prosodic information because of the intrinsic association of different
attributes, such as timbre and rhythm, and because of the need for supervised
training schemes to achieve robust large-scale and speaker-independent ASR. The
aim of this paper is to address the disentanglement of emotional prosody from
speech based on unsupervised reconstruction. Specifically, we identify, design,
implement and integrate three crucial components in our proposed speech
reconstruction model Prosody2Vec: (1) a unit encoder that transforms speech
signals into discrete units for semantic content, (2) a pretrained speaker
verification model to generate speaker identity embeddings, and (3) a trainable
prosody encoder to learn prosody representations. We first pretrain the
Prosody2Vec representations on unlabelled emotional speech corpora, then
fine-tune the model on specific datasets to perform Speech Emotion Recognition
(SER) and Emotional Voice Conversion (EVC) tasks. Both objective (weighted and
unweighted accuracies) and subjective (mean opinion score) evaluations on the
EVC task suggest that Prosody2Vec effectively captures general prosodic
features that can be smoothly transferred to other emotional speech. In
addition, our SER experiments on the IEMOCAP dataset reveal that the prosody
features learned by Prosody2Vec are complementary and beneficial for the
performance of widely used speech pretraining models and surpass the
state-of-the-art methods when combining Prosody2Vec with HuBERT
representations.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech, and Language
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Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification
Forensic audio analysis for speaker verification offers unique challenges due
to location/scenario uncertainty and diversity mismatch between reference and
naturalistic field recordings. The lack of real naturalistic forensic audio
corpora with ground-truth speaker identity represents a major challenge in this
field. It is also difficult to directly employ small-scale domain-specific data
to train complex neural network architectures due to domain mismatch and loss
in performance. Alternatively, cross-domain speaker verification for multiple
acoustic environments is a challenging task which could advance research in
audio forensics. In this study, we introduce a CRSS-Forensics audio dataset
collected in multiple acoustic environments. We pre-train a CNN-based network
using the VoxCeleb data, followed by an approach which fine-tunes part of the
high-level network layers with clean speech from CRSS-Forensics. Based on this
fine-tuned model, we align domain-specific distributions in the embedding space
with the discrepancy loss and maximum mean discrepancy (MMD). This maintains
effective performance on the clean set, while simultaneously generalizes the
model to other acoustic domains. From the results, we demonstrate that diverse
acoustic environments affect the speaker verification performance, and that our
proposed approach of cross-domain adaptation can significantly improve the
results in this scenario.Comment: To appear in INTERSPEECH 202
It's not what you say but the way that you say it: an fMRI study of differential lexical and non-lexical prosodic pitch processing
<p>Abstract</p> <p>Background</p> <p>This study aims to identify the neural substrate involved in prosodic pitch processing. Functional magnetic resonance imaging was used to test the premise that prosody pitch processing is primarily subserved by the right cortical hemisphere.</p> <p>Two experimental paradigms were used, firstly pairs of spoken sentences, where the only variation was a single internal phrase pitch change, and secondly, a matched condition utilizing pitch changes within analogous tone-sequence phrases. This removed the potential confounder of lexical evaluation. fMRI images were obtained using these paradigms.</p> <p>Results</p> <p>Activation was significantly greater within the right frontal and temporal cortices during the tone-sequence stimuli relative to the sentence stimuli.</p> <p>Conclusion</p> <p>This study showed that pitch changes, stripped of lexical information, are mainly processed by the right cerebral hemisphere, whilst the processing of analogous, matched, lexical pitch change is preferentially left sided. These findings, showing hemispherical differentiation of processing based on stimulus complexity, are in accord with a 'task dependent' hypothesis of pitch processing.</p
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