4,109 research outputs found
Training ASR models by Generation of Contextual Information
Supervised ASR models have reached unprecedented levels of accuracy, thanks
in part to ever-increasing amounts of labelled training data. However, in many
applications and locales, only moderate amounts of data are available, which
has led to a surge in semi- and weakly-supervised learning research. In this
paper, we conduct a large-scale study evaluating the effectiveness of
weakly-supervised learning for speech recognition by using loosely related
contextual information as a surrogate for ground-truth labels. For weakly
supervised training, we use 50k hours of public English social media videos
along with their respective titles and post text to train an encoder-decoder
transformer model. Our best encoder-decoder models achieve an average of 20.8%
WER reduction over a 1000 hours supervised baseline, and an average of 13.4%
WER reduction when using only the weakly supervised encoder for CTC
fine-tuning. Our results show that our setup for weak supervision improved both
the encoder acoustic representations as well as the decoder language generation
abilities
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives
where documents easily span over thousands of tokens. We propose a curriculum
learning (CL) based Pointer-Generator framework for reading/sampling over large
documents, enabling diverse training of the neural model based on the notion of
alternating contextual difficulty. This can be interpreted as a form of domain
randomization and/or generative pretraining during training. To this end, the
usage of the Pointer-Generator softens the requirement of having the answer
within the context, enabling us to construct diverse training samples for
learning. Additionally, we propose a new Introspective Alignment Layer (IAL),
which reasons over decomposed alignments using block-based self-attention. We
evaluate our proposed method on the NarrativeQA reading comprehension
benchmark, achieving state-of-the-art performance, improving existing baselines
by relative improvement on BLEU-4 and relative improvement on
Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and
CL components.Comment: Accepted to ACL 201
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Unsupervised intralingual and cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction
Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper firstly presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Secondly, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Thirdly, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation
Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
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