37,236 research outputs found
Bayesian Speaker Adaptation Based on a New Hierarchical Probabilistic Model
In this paper, a new hierarchical Bayesian speaker adaptation method called HMAP is proposed that combines the advantages of three conventional algorithms, maximum a posteriori (MAP), maximum-likelihood linear regression (MLLR), and eigenvoice, resulting in excellent performance across a wide range of adaptation conditions. The new method efficiently utilizes intra-speaker and inter-speaker correlation information through modeling phone and speaker subspaces in a consistent hierarchical Bayesian way. The phone variations for a specific speaker are assumed to be located in a low-dimensional subspace. The phone coordinate, which is shared among different speakers, implicitly contains the intra-speaker correlation information. For a specific speaker, the phone variation, represented by speaker-dependent eigenphones, are concatenated into a supervector. The eigenphone supervector space is also a low dimensional speaker subspace, which contains inter-speaker correlation information. Using principal component analysis (PCA), a new hierarchical probabilistic model for the generation of the speech observations is obtained. Speaker adaptation based on the new hierarchical model is derived using the maximum a posteriori criterion in a top-down manner. Both batch adaptation and online adaptation schemes are proposed. With tuned parameters, the new method can handle varying amounts of adaptation data automatically and efficiently. Experimental results on a Mandarin Chinese continuous speech recognition task show good performance under all testing conditions
ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
Current speaker verification techniques rely on a neural network to extract
speaker representations. The successful x-vector architecture is a Time Delay
Neural Network (TDNN) that applies statistics pooling to project
variable-length utterances into fixed-length speaker characterizing embeddings.
In this paper, we propose multiple enhancements to this architecture based on
recent trends in the related fields of face verification and computer vision.
Firstly, the initial frame layers can be restructured into 1-dimensional
Res2Net modules with impactful skip connections. Similarly to SE-ResNet, we
introduce Squeeze-and-Excitation blocks in these modules to explicitly model
channel interdependencies. The SE block expands the temporal context of the
frame layer by rescaling the channels according to global properties of the
recording. Secondly, neural networks are known to learn hierarchical features,
with each layer operating on a different level of complexity. To leverage this
complementary information, we aggregate and propagate features of different
hierarchical levels. Finally, we improve the statistics pooling module with
channel-dependent frame attention. This enables the network to focus on
different subsets of frames during each of the channel's statistics estimation.
The proposed ECAPA-TDNN architecture significantly outperforms state-of-the-art
TDNN based systems on the VoxCeleb test sets and the 2019 VoxCeleb Speaker
Recognition Challenge.Comment: proceedings of INTERSPEECH 202
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
Syllable classification using static matrices and prosodic features
In this paper we explore the usefulness of prosodic features for
syllable classification. In order to do this, we represent the
syllable as a static analysis unit such that its acoustic-temporal
dynamics could be merged into a set of features that the SVM
classifier will consider as a whole. In the first part of our
experiment we used MFCC as features for classification,
obtaining a maximum accuracy of 86.66%. The second part of
our study tests whether the prosodic information is
complementary to the cepstral information for syllable
classification. The results obtained show that combining the
two types of information does improve the classification, but
further analysis is necessary for a more successful
combination of the two types of features
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