74 research outputs found
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
Show from Tell: Audio-Visual Modelling in Clinical Settings
Auditory and visual signals usually present together and correlate with each
other, not only in natural environments but also in clinical settings. However,
the audio-visual modelling in the latter case can be more challenging, due to
the different sources of audio/video signals and the noise (both signal-level
and semantic-level) in auditory signals -- usually speech. In this paper, we
consider audio-visual modelling in a clinical setting, providing a solution to
learn medical representations that benefit various clinical tasks, without
human expert annotation. A simple yet effective multi-modal self-supervised
learning framework is proposed for this purpose. The proposed approach is able
to localise anatomical regions of interest during ultrasound imaging, with only
speech audio as a reference. Experimental evaluations on a large-scale clinical
multi-modal ultrasound video dataset show that the proposed self-supervised
method learns good transferable anatomical representations that boost the
performance of automated downstream clinical tasks, even outperforming
fully-supervised solutions
Show from Tell:Audio-Visual Modelling in Clinical Settings
Auditory and visual signals usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, the audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals -- usually speech. In this paper, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without human expert annotation. A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose. The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference. Experimental evaluations on a large-scale clinical multi-modal ultrasound video dataset show that the proposed self-supervised method learns good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions
Transparent authentication methodology in electronic education
In the context of on-line assessment in e-learning, a problem arises when a student taking an exam may wish to cheat by handing over personal credentials to someone else to take their place in an exam, Another problem is that there is no method for signing digital content as it is being produced in a computerized environment. Our proposed solution is to digitally sign the participantâs work by embedding voice samples in the transcript paper at regular intervals. In this investigation, we have demonstrated that a transparent stenographic methodology will provide an innovative and practical solution for achieving continuous authentication in an online educational environment by successful insertion and extraction of audio digital signatures
Dealing with training and test segmentation mismatch: FBK@IWSLT2021
This paper describes FBK's system submission to the IWSLT 2021 Offline Speech
Translation task. We participated with a direct model, which is a
Transformer-based architecture trained to translate English speech audio data
into German texts. The training pipeline is characterized by knowledge
distillation and a two-step fine-tuning procedure. Both knowledge distillation
and the first fine-tuning step are carried out on manually segmented real and
synthetic data, the latter being generated with an MT system trained on the
available corpora. Differently, the second fine-tuning step is carried out on a
random segmentation of the MuST-C v2 En-De dataset. Its main goal is to reduce
the performance drops occurring when a speech translation model trained on
manually segmented data (i.e. an ideal, sentence-like segmentation) is
evaluated on automatically segmented audio (i.e. actual, more realistic testing
conditions). For the same purpose, a custom hybrid segmentation procedure that
accounts for both audio content (pauses) and for the length of the produced
segments is applied to the test data before passing them to the system. At
inference time, we compared this procedure with a baseline segmentation method
based on Voice Activity Detection (VAD). Our results indicate the effectiveness
of the proposed hybrid approach, shown by a reduction of the gap with manual
segmentation from 8.3 to 1.4 BLEU points.Comment: Accepted at IWSLT202
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Energy disaggregation estimates appliance-by-appliance electricity
consumption from a single meter that measures the whole home's electricity
demand. Recently, deep neural networks have driven remarkable improvements in
classification performance in neighbouring machine learning fields such as
image classification and automatic speech recognition. In this paper, we adapt
three deep neural network architectures to energy disaggregation: 1) a form of
recurrent neural network called `long short-term memory' (LSTM); 2) denoising
autoencoders; and 3) a network which regresses the start time, end time and
average power demand of each appliance activation. We use seven metrics to test
the performance of these algorithms on real aggregate power data from five
appliances. Tests are performed against a house not seen during training and
against houses seen during training. We find that all three neural nets achieve
better F1 scores (averaged over all five appliances) than either combinatorial
optimisation or factorial hidden Markov models and that our neural net
algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou
Automatic speech feature extraction using a convolutional restricted boltzmann machine
A dissertation submitted to the Faculty of Science, University of
the Witwatersrand, in fulfillment of the requirements for the degree
of Master of Science
2017Restricted Boltzmann Machines (RBMs) are a statistical learning concept that can
be interpreted as Arti cial Neural Networks. They are capable of learning, in an
unsupervised fashion, a set of features with which to describe a data set. Connected
in series RBMs form a model called a Deep Belief Network (DBN), learning abstract
feature combinations from lower layers. Convolutional RBMs (CRBMs) are a variation
on the RBM architecture in which the learned features are kernels that are convolved
across spatial portions of the input data to generate feature maps identifying if a feature
is detected in a portion of the input data. Features extracted from speech audio data
by a trained CRBM have recently been shown to compete with the state of the art
for a number of speaker identi cation tasks. This project implements a similar CRBM
architecture in order to verify previous work, as well as gain insight into Digital Signal
Processing (DSP), Generative Graphical Models, unsupervised pre-training of Arti cial
Neural Networks, and Machine Learning classi cation tasks. The CRBM architecture
is trained on the TIMIT speech corpus and the learned features veri ed by using them
to train a linear classi er on tasks such as speaker genetic sex classi cation and speaker
identi cation. The implementation is quantitatively proven to successfully learn and
extract a useful feature representation for the given classi cation tasksMT 201
SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals
translate speech between any two languages? While recent breakthroughs in
text-based models have pushed machine translation coverage beyond 200
languages, unified speech-to-speech translation models have yet to achieve
similar strides. More specifically, conventional speech-to-speech translation
systems rely on cascaded systems that perform translation progressively,
putting high-performing unified systems out of reach. To address these gaps, we
introduce SeamlessM4T, a single model that supports speech-to-speech
translation, speech-to-text translation, text-to-speech translation,
text-to-text translation, and automatic speech recognition for up to 100
languages. To build this, we used 1 million hours of open speech audio data to
learn self-supervised speech representations with w2v-BERT 2.0. Subsequently,
we created a multimodal corpus of automatically aligned speech translations.
Filtered and combined with human-labeled and pseudo-labeled data, we developed
the first multilingual system capable of translating from and into English for
both speech and text. On FLEURS, SeamlessM4T sets a new standard for
translations into multiple target languages, achieving an improvement of 20%
BLEU over the previous SOTA in direct speech-to-text translation. Compared to
strong cascaded models, SeamlessM4T improves the quality of into-English
translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in
speech-to-speech. Tested for robustness, our system performs better against
background noises and speaker variations in speech-to-text tasks compared to
the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and
added toxicity to assess translation safety. Finally, all contributions in this
work are open-sourced and accessible at
https://github.com/facebookresearch/seamless_communicatio
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