23,656 research outputs found
KIT's Multilingual Speech Translation System for IWSLT 2023
Many existing speech translation benchmarks focus on native-English speech in
high-quality recording conditions, which often do not match the conditions in
real-life use-cases. In this paper, we describe our speech translation system
for the multilingual track of IWSLT 2023, which focuses on the translation of
scientific conference talks. The test condition features accented input speech
and terminology-dense contents. The tasks requires translation into 10
languages of varying amounts of resources. In absence of training data from the
target domain, we use a retrieval-based approach (kNN-MT) for effective
adaptation (+0.8 BLEU for speech translation). We also use adapters to easily
integrate incremental training data from data augmentation, and show that it
matches the performance of re-training. We observe that cascaded systems are
more easily adaptable towards specific target domains, due to their separate
modules. Our cascaded speech system substantially outperforms its end-to-end
counterpart on scientific talk translation, although their performance remains
similar on TED talks.Comment: IWSLT 202
Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
Encoder-decoder models provide a generic architecture for
sequence-to-sequence tasks such as speech recognition and translation. While
offline systems are often evaluated on quality metrics like word error rates
(WER) and BLEU, latency is also a crucial factor in many practical use-cases.
We propose three latency reduction techniques for chunk-based incremental
inference and evaluate their efficiency in terms of accuracy-latency trade-off.
On the 300-hour How2 dataset, we reduce latency by 83% to 0.8 second by
sacrificing 1% WER (6% rel.) compared to offline transcription. Although our
experiments use the Transformer, the hypothesis selection strategies are
applicable to other encoder-decoder models. To avoid expensive re-computation,
we use a unidirectionally-attending encoder. After an adaptation procedure to
partial sequences, the unidirectional model performs on-par with the original
model. We further show that our approach is also applicable to low-latency
speech translation. On How2 English-Portuguese speech translation, we reduce
latency to 0.7 second (-84% rel.) while incurring a loss of 2.4 BLEU points (5%
rel.) compared to the offline system
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
Domain adaptation strategies in statistical machine translation: a brief overview
© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft
Relative Positional Encoding for Speech Recognition and Direct Translation
Transformer models are powerful sequence-to-sequence architectures that are
capable of directly mapping speech inputs to transcriptions or translations.
However, the mechanism for modeling positions in this model was tailored for
text modeling, and thus is less ideal for acoustic inputs. In this work, we
adapt the relative position encoding scheme to the Speech Transformer, where
the key addition is relative distance between input states in the
self-attention network. As a result, the network can better adapt to the
variable distributions present in speech data. Our experiments show that our
resulting model achieves the best recognition result on the Switchboard
benchmark in the non-augmentation condition, and the best published result in
the MuST-C speech translation benchmark. We also show that this model is able
to better utilize synthetic data than the Transformer, and adapts better to
variable sentence segmentation quality for speech translation.Comment: Submitted to Interspeech 202
Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks
Deep neural networks (DNNs) have become a widely deployed model for numerous
machine learning applications. However, their fixed architecture, substantial
training cost, and significant model redundancy make it difficult to
efficiently update them to accommodate previously unseen data. To solve these
problems, we propose an incremental learning framework based on a
grow-and-prune neural network synthesis paradigm. When new data arrive, the
neural network first grows new connections based on the gradients to increase
the network capacity to accommodate new data. Then, the framework iteratively
prunes away connections based on the magnitude of weights to enhance network
compactness, and hence recover efficiency. Finally, the model rests at a
lightweight DNN that is both ready for inference and suitable for future
grow-and-prune updates. The proposed framework improves accuracy, shrinks
network size, and significantly reduces the additional training cost for
incoming data compared to conventional approaches, such as training from
scratch and network fine-tuning. For the LeNet-300-100 and LeNet-5 neural
network architectures derived for the MNIST dataset, the framework reduces
training cost by up to 64% (63%) and 67% (63%) compared to training from
scratch (network fine-tuning), respectively. For the ResNet-18 architecture
derived for the ImageNet dataset and DeepSpeech2 for the AN4 dataset, the
corresponding training cost reductions against training from scratch (network
fine-tunning) are 64% (60%) and 67% (62%), respectively. Our derived models
contain fewer network parameters but achieve higher accuracy relative to
conventional baselines
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
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