61 research outputs found
Accurate Detection of Wake Word Start and End Using a CNN
Small footprint embedded devices require keyword spotters (KWS) with small
model size and detection latency for enabling voice assistants. Such a keyword
is often referred to as \textit{wake word} as it is used to wake up voice
assistant enabled devices. Together with wake word detection, accurate
estimation of wake word endpoints (start and end) is an important task of KWS.
In this paper, we propose two new methods for detecting the endpoints of wake
words in neural KWS that use single-stage word-level neural networks. Our
results show that the new techniques give superior accuracy for detecting wake
words' endpoints of up to 50 msec standard error versus human annotations, on
par with the conventional Acoustic Model plus HMM forced alignment. To our
knowledge, this is the first study of wake word endpoints detection methods for
single-stage neural KWS.Comment: Proceedings of INTERSPEEC
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili
We consider hate speech detection through keyword spotting on radio
broadcasts. One approach is to build an automatic speech recognition (ASR)
system for the target low-resource language. We compare this to using acoustic
word embedding (AWE) models that map speech segments to a space where matching
words have similar vectors. We specifically use a multilingual AWE model
trained on labelled data from well-resourced languages to spot keywords in data
in the unseen target language. In contrast to ASR, the AWE approach only
requires a few keyword exemplars. In controlled experiments on Wolof and
Swahili where training and test data are from the same domain, an ASR model
trained on just five minutes of data outperforms the AWE approach. But in an
in-the-wild test on Swahili radio broadcasts with actual hate speech keywords,
the AWE model (using one minute of template data) is more robust, giving
similar performance to an ASR system trained on 30 hours of labelled data.Comment: Accepted to Interspeech 202
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition
Building conversational speech recognition systems for new languages is
constrained by the availability of utterances that capture user-device
interactions. Data collection is both expensive and limited by the speed of
manual transcription. In order to address this, we advocate the use of neural
machine translation as a data augmentation technique for bootstrapping language
models. Machine translation (MT) offers a systematic way of incorporating
collections from mature, resource-rich conversational systems that may be
available for a different language. However, ingesting raw translations from a
general purpose MT system may not be effective owing to the presence of named
entities, intra sentential code-switching and the domain mismatch between the
conversational data being translated and the parallel text used for MT
training. To circumvent this, we explore the following domain adaptation
techniques: (a) sentence embedding based data selection for MT training, (b)
model finetuning, and (c) rescoring and filtering translated hypotheses. Using
Hindi as the experimental testbed, we translate US English utterances to
supplement the transcribed collections. We observe a relative word error rate
reduction of 7.8-15.6%, depending on the bootstrapping phase. Fine grained
analysis reveals that translation particularly aids the interaction scenarios
which are underrepresented in the transcribed data.Comment: Accepted by IEEE ASRU workshop, 201
Low-resource speech translation
We explore the task of speech-to-text translation (ST), where speech in one language
(source) is converted to text in a different one (target). Traditional ST systems go
through an intermediate step where the source language speech is first converted to
source language text using an automatic speech recognition (ASR) system, which
is then converted to target language text using a machine translation (MT) system.
However, this pipeline based approach is impractical for unwritten languages spoken by
millions of people around the world, leaving them without access to free and automated
translation services such as Google Translate. The lack of such translation services can
have important real-world consequences. For example, in the aftermath of a disaster
scenario, easily available translation services can help better co-ordinate relief efforts.
How can we expand the coverage of automated ST systems to include scenarios which
lack source language text? In this thesis we investigate one possible solution: we
build ST systems to directly translate source language speech into target language text,
thereby forgoing the dependency on source language text. To build such a system, we
use only speech data paired with text translations as training data. We also specifically
focus on low-resource settings, where we expect at most tens of hours of training data
to be available for unwritten or endangered languages.
Our work can be broadly divided into three parts. First we explore how we can leverage
prior work to build ST systems. We find that neural sequence-to-sequence models are
an effective and convenient method for ST, but produce poor quality translations when
trained in low-resource settings.
In the second part of this thesis, we explore methods to improve the translation performance
of our neural ST systems which do not require labeling additional speech
data in the low-resource language, a potentially tedious and expensive process. Instead
we exploit labeled speech data for high-resource languages which is widely available
and relatively easier to obtain. We show that pretraining a neural model with ASR data
from a high-resource language, different from both the source and target ST languages,
improves ST performance.
In the final part of our thesis, we study whether ST systems can be used to build
applications which have traditionally relied on the availability of ASR systems, such
as information retrieval, clustering audio documents, or question/answering. We build
proof-of-concept systems for two downstream applications: topic prediction for speech
and cross-lingual keyword spotting. Our results indicate that low-resource ST systems
can still outperform simple baselines for these tasks, leaving the door open for further
exploratory work.
This thesis provides, for the first time, an in-depth study of neural models for the
task of direct ST across a range of training data settings on a realistic multi-speaker
speech corpus. Our contributions include a set of open-source tools to encourage further
research
A Review of Deep Learning Techniques for Speech Processing
The field of speech processing has undergone a transformative shift with the
advent of deep learning. The use of multiple processing layers has enabled the
creation of models capable of extracting intricate features from speech data.
This development has paved the way for unparalleled advancements in speech
recognition, text-to-speech synthesis, automatic speech recognition, and
emotion recognition, propelling the performance of these tasks to unprecedented
heights. The power of deep learning techniques has opened up new avenues for
research and innovation in the field of speech processing, with far-reaching
implications for a range of industries and applications. This review paper
provides a comprehensive overview of the key deep learning models and their
applications in speech-processing tasks. We begin by tracing the evolution of
speech processing research, from early approaches, such as MFCC and HMM, to
more recent advances in deep learning architectures, such as CNNs, RNNs,
transformers, conformers, and diffusion models. We categorize the approaches
and compare their strengths and weaknesses for solving speech-processing tasks.
Furthermore, we extensively cover various speech-processing tasks, datasets,
and benchmarks used in the literature and describe how different deep-learning
networks have been utilized to tackle these tasks. Additionally, we discuss the
challenges and future directions of deep learning in speech processing,
including the need for more parameter-efficient, interpretable models and the
potential of deep learning for multimodal speech processing. By examining the
field's evolution, comparing and contrasting different approaches, and
highlighting future directions and challenges, we hope to inspire further
research in this exciting and rapidly advancing field
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
Neural approaches to spoken content embedding
Comparing spoken segments is a central operation to speech processing.
Traditional approaches in this area have favored frame-level dynamic
programming algorithms, such as dynamic time warping, because they require no
supervision, but they are limited in performance and efficiency. As an
alternative, acoustic word embeddings -- fixed-dimensional vector
representations of variable-length spoken word segments -- have begun to be
considered for such tasks as well. However, the current space of such
discriminative embedding models, training approaches, and their application to
real-world downstream tasks is limited. We start by considering ``single-view"
training losses where the goal is to learn an acoustic word embedding model
that separates same-word and different-word spoken segment pairs. Then, we
consider ``multi-view" contrastive losses. In this setting, acoustic word
embeddings are learned jointly with embeddings of character sequences to
generate acoustically grounded embeddings of written words, or acoustically
grounded word embeddings.
In this thesis, we contribute new discriminative acoustic word embedding
(AWE) and acoustically grounded word embedding (AGWE) approaches based on
recurrent neural networks (RNNs). We improve model training in terms of both
efficiency and performance. We take these developments beyond English to
several low-resource languages and show that multilingual training improves
performance when labeled data is limited. We apply our embedding models, both
monolingual and multilingual, to the downstream tasks of query-by-example
speech search and automatic speech recognition. Finally, we show how our
embedding approaches compare with and complement more recent self-supervised
speech models.Comment: PhD thesi
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
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