8 research outputs found
Adaptive Feature Selection for End-to-End Speech Translation
Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features. In this paper, we propose adaptive feature selection (AFS) for encoder-decoder based E2E ST. We first pre-train an ASR encoder and apply AFS to dynamically estimate the importance of each encoded speech feature to ASR. A ST encoder, stacked on top of the ASR encoder, then receives the filtered features from the (frozen) ASR encoder. We take L0DROP (Zhang et al., 2020) as the backbone for AFS, and adapt it to sparsify speech features with respect to both temporal and feature dimensions. Results on LibriSpeech EnFr and MuST-C benchmarks show that AFS facilitates learning of ST by pruning out ~84% temporal features, yielding an average translation gain of ~1.3-1.6 BLEU and a decoding speedup of ~1.4x. In particular, AFS reduces the performance gap compared to the cascade baseline, and outperforms it on LibriSpeech En-Fr with a BLEU score of 18.56 (without data augmentation)
Consecutive Decoding for Speech-to-text Translation
Speech-to-text translation (ST), which directly translates the source
language speech to the target language text, has attracted intensive attention
recently. However, the combination of speech recognition and machine
translation in a single model poses a heavy burden on the direct cross-modal
cross-lingual mapping. To reduce the learning difficulty, we propose
COnSecutive Transcription and Translation (COSTT), an integral approach for
speech-to-text translation. The key idea is to generate source transcript and
target translation text with a single decoder. It benefits the model training
so that additional large parallel text corpus can be fully exploited to enhance
the speech translation training. Our method is verified on three mainstream
datasets, including Augmented LibriSpeech English-French dataset, TED
English-German dataset, and TED English-Chinese dataset. Experiments show that
our proposed COSTT outperforms the previous state-of-the-art methods. The code
is available at https://github.com/dqqcasia/st.Comment: Accepted by AAAI 2021. arXiv admin note: text overlap with
arXiv:2009.0970
Deliberation Model Based Two-Pass End-to-End Speech Recognition
End-to-end (E2E) models have made rapid progress in automatic speech
recognition (ASR) and perform competitively relative to conventional models. To
further improve the quality, a two-pass model has been proposed to rescore
streamed hypotheses using the non-streaming Listen, Attend and Spell (LAS)
model while maintaining a reasonable latency. The model attends to acoustics to
rescore hypotheses, as opposed to a class of neural correction models that use
only first-pass text hypotheses. In this work, we propose to attend to both
acoustics and first-pass hypotheses using a deliberation network. A
bidirectional encoder is used to extract context information from first-pass
hypotheses. The proposed deliberation model achieves 12% relative WER reduction
compared to LAS rescoring in Google Voice Search (VS) tasks, and 23% reduction
on a proper noun test set. Compared to a large conventional model, our best
model performs 21% relatively better for VS. In terms of computational
complexity, the deliberation decoder has a larger size than the LAS decoder,
and hence requires more computations in second-pass decoding
"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
An end-to-end speech-to-text translation (ST) takes audio in a source
language and outputs the text in a target language. Existing methods are
limited by the amount of parallel corpus. Can we build a system to fully
utilize signals in a parallel ST corpus? We are inspired by human understanding
system which is composed of auditory perception and cognitive processing. In
this paper, we propose Listen-Understand-Translate, (LUT), a unified framework
with triple supervision signals to decouple the end-to-end speech-to-text
translation task. LUT is able to guide the acoustic encoder to extract as much
information from the auditory input. In addition, LUT utilizes a pre-trained
BERT model to enforce the upper encoder to produce as much semantic information
as possible, without extra data. We perform experiments on a diverse set of
speech translation benchmarks, including Librispeech English-French, IWSLT
English-German and TED English-Chinese. Our results demonstrate LUT achieves
the state-of-the-art performance, outperforming previous methods. The code is
available at https://github.com/dqqcasia/st.Comment: Accepted by AAAI 202
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