47 research outputs found
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook
In recent years, reinforcement learning and bandits have transformed a wide
range of real-world applications including healthcare, finance, recommendation
systems, robotics, and last but not least, the speech and natural language
processing. While most speech and language applications of reinforcement
learning algorithms are centered around improving the training of deep neural
networks with its flexible optimization properties, there are still many
grounds to explore to utilize the benefits of reinforcement learning, such as
its reward-driven adaptability, state representations, temporal structures and
generalizability. In this survey, we present an overview of recent advancements
of reinforcement learning and bandits, and discuss how they can be effectively
employed to solve speech and natural language processing problems with models
that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying
INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in
large language models (LLMs
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
Audio self-supervised learning: a survey
Inspired by the humans' cognitive ability to generalise knowledge and skills,
Self-Supervised Learning (SSL) targets at discovering general representations
from large-scale data without requiring human annotations, which is an
expensive and time consuming task. Its success in the fields of computer vision
and natural language processing have prompted its recent adoption into the
field of audio and speech processing. Comprehensive reviews summarising the
knowledge in audio SSL are currently missing. To fill this gap, in the present
work, we provide an overview of the SSL methods used for audio and speech
processing applications. Herein, we also summarise the empirical works that
exploit the audio modality in multi-modal SSL frameworks, and the existing
suitable benchmarks to evaluate the power of SSL in the computer audition
domain. Finally, we discuss some open problems and point out the future
directions on the development of audio SSL
Speech segmentation and speaker diarisation for transcription and translation
This dissertation outlines work related to Speech Segmentation – segmenting an audio
recording into regions of speech and non-speech, and Speaker Diarization – further
segmenting those regions into those pertaining to homogeneous speakers.
Knowing not only what was said but also who said it and when, has many useful
applications. As well as providing a richer level of transcription for speech, we will
show how such knowledge can improve Automatic Speech Recognition (ASR) system
performance and can also benefit downstream Natural Language Processing (NLP)
tasks such as machine translation and punctuation restoration.
While segmentation and diarization may appear to be relatively simple tasks to
describe, in practise we find that they are very challenging and are, in general, ill-defined
problems. Therefore, we first provide a formalisation of each of the problems
as the sub-division of speech within acoustic space and time. Here, we see that the
task can become very difficult when we want to partition this domain into our target
classes of speakers, whilst avoiding other classes that reside in the same space, such as
phonemes. We present a theoretical framework for describing and discussing the tasks
as well as introducing existing state-of-the-art methods and research.
Current Speaker Diarization systems are notoriously sensitive to hyper-parameters
and lack robustness across datasets. Therefore, we present a method which uses a series
of oracle experiments to expose the limitations of current systems and to which
system components these limitations can be attributed. We also demonstrate how Diarization
Error Rate (DER), the dominant error metric in the literature, is not a comprehensive
or reliable indicator of overall performance or of error propagation to subsequent
downstream tasks. These results inform our subsequent research.
We find that, as a precursor to Speaker Diarization, the task of Speech Segmentation
is a crucial first step in the system chain. Current methods typically do not account
for the inherent structure of spoken discourse. As such, we explored a novel method
which exploits an utterance-duration prior in order to better model the segment distribution
of speech. We show how this method improves not only segmentation, but also
the performance of subsequent speech recognition, machine translation and speaker
diarization systems.
Typical ASR transcriptions do not include punctuation and the task of enriching
transcriptions with this information is known as ‘punctuation restoration’. The benefit
is not only improved readability but also better compatibility with NLP systems
that expect sentence-like units such as in conventional machine translation. We show
how segmentation and diarization are related tasks that are able to contribute acoustic
information that complements existing linguistically-based punctuation approaches.
There is a growing demand for speech technology applications in the broadcast media
domain. This domain presents many new challenges including diverse noise and
recording conditions. We show that the capacity of existing GMM-HMM based speech
segmentation systems is limited for such scenarios and present a Deep Neural Network
(DNN) based method which offers a more robust speech segmentation method resulting
in improved speech recognition performance for a television broadcast dataset.
Ultimately, we are able to show that the speech segmentation is an inherently ill-defined
problem for which the solution is highly dependent on the downstream task
that it is intended for
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure