16,146 research outputs found
Cross-lingual dysphonic speech detection using pretrained speaker embeddings
In this study, cross-lingual binary classification and severity estimation of dysphonic speech have been carried out. Hand-crafted acoustic feature extraction is replaced by the speaker embedding techniques used in the speaker verification. Two state of art deep learning methods for speaker verification have been used: the X-vector and ECAPA-TDNN. Embeddings are extracted from speech samples in Hungarian and Dutch languages and used to train Support Vector Machine (SVM) and Support Vector Regressor (SVR) for binary classification and severity estimation, in a cross-language manner. Our results were competitive with manual feature engineering, when the models were trained on Hungarian samples and evaluated on Dutch samples in the binary classification of dysphonic speech and outperformed in estimating the severity level of dysphonic speech. Moreover, our model achieved 0.769 and 0.771 in Spearman and Pearson correlations. Also, our results in both classification and regression were superior compared to manual feature extraction technique when models were trained on Dutch samples and evaluated on Hungarian samples with only a limited number of samples are available for training. An accuracy of 86.8% was reached with features extracted from embedding methods, while the maximum accuracy using hand-crafted acoustic features was 66.8%. Overall results show that Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) performs better than the former X-vector in both tasks
Deep clustering: Discriminative embeddings for segmentation and separation
We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains.Comment: Originally submitted on June 5, 201
Deep attractor network for single-microphone speaker separation
Despite the overwhelming success of deep learning in various speech
processing tasks, the problem of separating simultaneous speakers in a mixture
remains challenging. Two major difficulties in such systems are the arbitrary
source permutation and unknown number of sources in the mixture. We propose a
novel deep learning framework for single channel speech separation by creating
attractor points in high dimensional embedding space of the acoustic signals
which pull together the time-frequency bins corresponding to each source.
Attractor points in this study are created by finding the centroids of the
sources in the embedding space, which are subsequently used to determine the
similarity of each bin in the mixture to each source. The network is then
trained to minimize the reconstruction error of each source by optimizing the
embeddings. The proposed model is different from prior works in that it
implements an end-to-end training, and it does not depend on the number of
sources in the mixture. Two strategies are explored in the test time, K-means
and fixed attractor points, where the latter requires no post-processing and
can be implemented in real-time. We evaluated our system on Wall Street Journal
dataset and show 5.49\% improvement over the previous state-of-the-art methods.Comment: 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
NPLDA: A Deep Neural PLDA Model for Speaker Verification
The state-of-art approach for speaker verification consists of a neural
network based embedding extractor along with a backend generative model such as
the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose
a neural network approach for backend modeling in speaker recognition. The
likelihood ratio score of the generative PLDA model is posed as a
discriminative similarity function and the learnable parameters of the score
function are optimized using a verification cost. The proposed model, termed as
neural PLDA (NPLDA), is initialized using the generative PLDA model parameters.
The loss function for the NPLDA model is an approximation of the minimum
detection cost function (DCF). The speaker recognition experiments using the
NPLDA model are performed on the speaker verificiation task in the VOiCES
datasets as well as the SITW challenge dataset. In these experiments, the NPLDA
model optimized using the proposed loss function improves significantly over
the state-of-art PLDA based speaker verification system.Comment: Published in Odyssey 2020, the Speaker and Language Recognition
Workshop (VOiCES Special Session). Link to GitHub Implementation:
https://github.com/iiscleap/NeuralPlda. arXiv admin note: substantial text
overlap with arXiv:2001.0703
- …