3,039 research outputs found
Latest trends in hybrid machine translation and its applications
This survey on hybrid machine translation (MT) is motivated by the fact that hybridization techniques have become popular as they attempt to combine the best characteristics of highly advanced pure rule or corpus-based MT approaches. Existing research typically covers either simple or more complex architectures guided by either rule or corpus-based approaches. The goal is to combine the best properties of each type.
This survey provides a detailed overview of the modification of the standard rule-based architecture to include statistical knowl- edge, the introduction of rules in corpus-based approaches, and the hybridization of approaches within this last single category. The principal aim here is to cover the leading research and progress in this field of MT and in several related applications.Peer ReviewedPostprint (published version
Semi-Autoregressive Streaming ASR With Label Context
Non-autoregressive (NAR) modeling has gained significant interest in speech
processing since these models achieve dramatically lower inference time than
autoregressive (AR) models while also achieving good transcription accuracy.
Since NAR automatic speech recognition (ASR) models must wait for the
completion of the entire utterance before processing, some works explore
streaming NAR models based on blockwise attention for low-latency applications.
However, streaming NAR models significantly lag in accuracy compared to
streaming AR and non-streaming NAR models. To address this, we propose a
streaming "semi-autoregressive" ASR model that incorporates the labels emitted
in previous blocks as additional context using a Language Model (LM)
subnetwork. We also introduce a novel greedy decoding algorithm that addresses
insertion and deletion errors near block boundaries while not significantly
increasing the inference time. Experiments show that our method outperforms the
existing streaming NAR model by 19% relative on Tedlium2, 16%/8% on
Librispeech-100 clean/other test sets, and 19%/8% on the Switchboard(SWB) /
Callhome(CH) test sets. It also reduced the accuracy gap with streaming AR and
non-streaming NAR models while achieving 2.5x lower latency. We also
demonstrate that our approach can effectively utilize external text data to
pre-train the LM subnetwork to further improve streaming ASR accuracy.Comment: Submitted to ICASSP 202
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers
End-to-end formulation of automatic speech recognition (ASR) and speech
translation (ST) makes it easy to use a single model for both multilingual ASR
and many-to-many ST. In this paper, we propose streaming language-agnostic
multilingual speech recognition and translation using neural transducers
(LAMASSU). To enable multilingual text generation in LAMASSU, we conduct a
systematic comparison between specified and unified prediction and joint
networks. We leverage a language-agnostic multilingual encoder that
substantially outperforms shared encoders. To enhance LAMASSU, we propose to
feed target LID to encoders. We also apply connectionist temporal
classification regularization to transducer training. Experimental results show
that LAMASSU not only drastically reduces the model size but also outperforms
monolingual ASR and bilingual ST models.Comment: Submitted to ICASSP 202
Image speech combination for interactive computer assisted transcription of handwritten documents
[EN] Handwritten document transcription aims to obtain the contents of a document to provide efficient information access to, among other, digitised historical documents. The increasing number of historical documents published by libraries and archives makes this an important task. In this context, the use of image processing and understanding techniques in conjunction with assistive technologies reduces the time and human effort required for obtaining the final perfect transcription. The assistive transcription system proposes a hypothesis, usually derived from a recognition process of the handwritten text image. Then, the professional transcriber feedback can be used to obtain an improved hypothesis and speed-up the final transcription. In this framework, a speech signal corresponding to the dictation of the handwritten text can be used as an additional source of information. This multimodal approach, that combines the image of the handwritten text with the speech of the dictation of its contents, could make better the hypotheses (initial and improved) offered to the transcriber. In this paper we study the feasibility of a multimodal interactive transcription system for an assistive paradigm known as Computer Assisted Transcription of Text Images. Different techniques are tested for obtaining the multimodal combination in this framework. The use of the proposed multimodal approach reveals a significant reduction of transcription effort with some multimodal combination techniques, allowing for a faster transcription process.Work partially supported by projects READ-674943 (European Union's H2020), SmartWays-RTC-2014-1466-4 (MINECO, Spain), and CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER), and by Generalitat Valenciana (GVA), Spain under reference PROMETEOII/2014/030.Granell, E.; Romero, V.; MartÃnez-Hinarejos, C. (2019). Image speech combination for interactive computer assisted transcription of handwritten documents. Computer Vision and Image Understanding. 180:74-83. https://doi.org/10.1016/j.cviu.2019.01.009S748318
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