483 research outputs found
Probabilistic Latent Variable Models as Nonnegative Factorizations
This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix
factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order decompositions and sparsity constraints. We argue through these extensions that the use of this approach allows for rapid development of complex statistical models for analyzing nonnegative data
CTC-based Non-autoregressive Speech Translation
Combining end-to-end speech translation (ST) and non-autoregressive (NAR)
generation is promising in language and speech processing for their advantages
of less error propagation and low latency. In this paper, we investigate the
potential of connectionist temporal classification (CTC) for non-autoregressive
speech translation (NAST). In particular, we develop a model consisting of two
encoders that are guided by CTC to predict the source and target texts,
respectively. Introducing CTC into NAST on both language sides has obvious
challenges: 1) the conditional independent generation somewhat breaks the
interdependency among tokens, and 2) the monotonic alignment assumption in
standard CTC does not hold in translation tasks. In response, we develop a
prediction-aware encoding approach and a cross-layer attention approach to
address these issues. We also use curriculum learning to improve convergence of
training. Experiments on the MuST-C ST benchmarks show that our NAST model
achieves an average BLEU score of 29.5 with a speed-up of 5.67, which
is comparable to the autoregressive counterpart and even outperforms the
previous best result of 0.9 BLEU points.Comment: ACL 2023 Main Conferenc
Joint morphological-lexical language modeling for processing morphologically rich languages with application to dialectal Arabic
Language modeling for an inflected language
such as Arabic poses new challenges for speech recognition and
machine translation due to its rich morphology. Rich morphology
results in large increases in out-of-vocabulary (OOV) rate and
poor language model parameter estimation in the absence of large
quantities of data. In this study, we present a joint
morphological-lexical language model (JMLLM) that takes
advantage of Arabic morphology. JMLLM combines
morphological segments with the underlying lexical items and
additional available information sources with regards to
morphological segments and lexical items in a single joint model.
Joint representation and modeling of morphological and lexical
items reduces the OOV rate and provides smooth probability
estimates while keeping the predictive power of whole words.
Speech recognition and machine translation experiments in
dialectal-Arabic show improvements over word and morpheme
based trigram language models. We also show that as the
tightness of integration between different information sources
increases, both speech recognition and machine translation
performances improve
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi
Audiovisual head orientation estimation with particle filtering in multisensor scenarios
This article presents a multimodal approach to head pose estimation of individuals in environments equipped with multiple cameras and microphones, such as SmartRooms or automatic video conferencing. Determining the individuals head orientation is the basis for many forms of more sophisticated interactions between humans and technical devices and can also be used for automatic sensor selection (camera, microphone) in communications or video surveillance systems. The use of particle filters as a unified framework for the estimation of the head orientation for both monomodal and multimodal cases is proposed. In video, we estimate head orientation from color information by exploiting spatial redundancy among cameras. Audio information is processed to estimate the direction of the voice produced by a speaker making use of the directivity characteristics of the head radiation pattern. Furthermore, two different particle filter multimodal information fusion schemes for combining the audio and video streams are analyzed in terms of accuracy and robustness. In the first one, fusion is performed at a decision level by combining each monomodal head pose estimation, while the second one uses a joint estimation system combining information at data level. Experimental results conducted over the CLEAR 2006 evaluation database are reported and the comparison of the proposed multimodal head pose estimation algorithms with the reference monomodal approaches proves the effectiveness of the proposed approach.Postprint (published version
Pheme: Efficient and Conversational Speech Generation
In recent years, speech generation has seen remarkable progress, now
achieving one-shot generation capability that is often virtually
indistinguishable from real human voice. Integrating such advancements in
speech generation with large language models might revolutionize a wide range
of applications. However, certain applications, such as assistive
conversational systems, require natural and conversational speech generation
tools that also operate efficiently in real time. Current state-of-the-art
models like VALL-E and SoundStorm, powered by hierarchical neural audio codecs,
require large neural components and extensive training data to work well. In
contrast, MQTTS aims to build more compact conversational TTS models while
capitalizing on smaller-scale real-life conversational speech data. However,
its autoregressive nature yields high inference latency and thus limits its
real-time usage. In order to mitigate the current limitations of the
state-of-the-art TTS models while capitalizing on their strengths, in this work
we introduce the Pheme model series that 1) offers compact yet high-performing
models, 2) allows for parallel speech generation of 3) natural conversational
speech, and 4) it can be trained efficiently on smaller-scale conversational
data, cutting data demands by more than 10x but still matching the quality of
the autoregressive TTS models. We also show that through simple teacher-student
distillation we can meet significant improvements in voice quality for
single-speaker setups on top of pretrained Pheme checkpoints, relying solely on
synthetic speech generated by much larger teacher models. Audio samples and
pretrained models are available online
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