14,909 research outputs found
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST
speaker recognition evaluations (SRE). The latest edition of such joint
submission was in SRE 2018, in which the I4U submission was among the
best-performing systems. SRE'18 also marks the 10-year anniversary of I4U
consortium into NIST SRE series of evaluation. The primary objective of the
current paper is to summarize the results and lessons learned based on the
twelve sub-systems and their fusion submitted to SRE'18. It is also our
intention to present a shared view on the advancements, progresses, and major
paradigm shifts that we have witnessed as an SRE participant in the past decade
from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm
shift from supervector representation to deep speaker embedding, and a switch
of research challenge from channel compensation to domain adaptation.Comment: 5 page
Collapsed speech segment detection and suppression for WaveNet vocoder
In this paper, we propose a technique to alleviate the quality degradation
caused by collapsed speech segments sometimes generated by the WaveNet vocoder.
The effectiveness of the WaveNet vocoder for generating natural speech from
acoustic features has been proved in recent works. However, it sometimes
generates very noisy speech with collapsed speech segments when only a limited
amount of training data is available or significant acoustic mismatches exist
between the training and testing data. Such a limitation on the corpus and
limited ability of the model can easily occur in some speech generation
applications, such as voice conversion and speech enhancement. To address this
problem, we propose a technique to automatically detect collapsed speech
segments. Moreover, to refine the detected segments, we also propose a waveform
generation technique for WaveNet using a linear predictive coding constraint.
Verification and subjective tests are conducted to investigate the
effectiveness of the proposed techniques. The verification results indicate
that the detection technique can detect most collapsed segments. The subjective
evaluations of voice conversion demonstrate that the generation technique
significantly improves the speech quality while maintaining the same speaker
similarity.Comment: 5 pages, 6 figures. Proc. Interspeech, 201
Learnable PINs: Cross-Modal Embeddings for Person Identity
We propose and investigate an identity sensitive joint embedding of face and
voice. Such an embedding enables cross-modal retrieval from voice to face and
from face to voice. We make the following four contributions: first, we show
that the embedding can be learnt from videos of talking faces, without
requiring any identity labels, using a form of cross-modal self-supervision;
second, we develop a curriculum learning schedule for hard negative mining
targeted to this task, that is essential for learning to proceed successfully;
third, we demonstrate and evaluate cross-modal retrieval for identities unseen
and unheard during training over a number of scenarios and establish a
benchmark for this novel task; finally, we show an application of using the
joint embedding for automatically retrieving and labelling characters in TV
dramas.Comment: To appear in ECCV 201
Robust language recognition via adaptive language factor extraction
This paper presents a technique to adapt an acoustically based
language classifier to the background conditions and speaker
accents. This adaptation improves language classification on
a broad spectrum of TV broadcasts. The core of the system
consists of an iVector-based setup in which language and channel
variabilities are modeled separately. The subsequent language
classifier (the backend) operates on the language factors,
i.e. those features in the extracted iVectors that explain the observed
language variability. The proposed technique adapts the
language variability model to the background conditions and
to the speaker accents present in the audio. The effect of the
adaptation is evaluated on a 28 hours corpus composed of documentaries and monolingual as well as multilingual broadcast
news shows. Consistent improvements in the automatic identification
of Flemish (Belgian Dutch), English and French are demonstrated for all broadcast types
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