Anjuli Kannan
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Slide Gestalt: Automatic Structure Extraction in Slide Decks for Non-Visual Access
Yi-Hao Peng
CHI 2023: ACM Conference on Human Factors in Computing Systems (2023) (to appear)
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Presentation slides commonly use visual patterns for structural navigation, such as titles, dividers, and build slides. However, screen readers do not capture such intention, making it time-consuming and less accessible for blind and visually impaired (BVI) users to linearly consume slides with repeated content. We present Slide Gestalt, an automatic approach that identifies the hierarchical structure in a slide deck. Slide Gestalt computes the visual and textual correspondences between slides to generate hierarchical groupings. Readers can navigate the slide deck from the higher-level section overview to the lower-level description of a slide group or individual elements interactively with our UI. We derived side consumption and authoring practices from interviews with BVI readers and sighted creators and an analysis of 100 decks. We performed our pipeline with 50 real-world slide decks and a large dataset. Feedback from eight BVI participants showed that Slide Gestalt helped navigate a slide deck by anchoring content more efficiently, compared to using accessible slides.
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Automated speech recognition (ASR) coverage of the world's languages continues to expand. Yet, as data-demanding neural network models continue to revolutionize the field, it poses a challenge for data-scarce languages. Multilingual models allow for the joint training of data-scarce and data-rich languages enabling data and parameter sharing. One of the main goals of multilingual ASR is to build a single model for all languages while reaping the benefits of sharing on data-scarce languages without impacting performance on the data-rich languages. However, most state-of-the-art multilingual models require the encoding of language information and therefore are not as flexible or scalable when expanding to newer languages. Language independent multilingual models help to address this, as well as, are more suited to multicultural societies such as in India, where languages overlap and are frequently used together by native speakers. In this paper, we propose a new approach to building a language-agnostic multilingual ASR system using transliteration. This training strategy maps all languages to one writing system through a many-to-one transliteration transducer that maps similar sounding acoustics to one target sequences such as, graphemes, phonemes or wordpieces resulting in improved data sharing and reduced phonetic confusions. We propose a training strategy that maps all languages to one writing system through a many-to-one transliteration transducer. We show with four Indic languages, namely, Hindi, Bengali, Tamil and Kannada, that the resulting multilingual model achieves a performance comparable to a language-dependent multilingual model, with an improvement of up to 15\% relative on the data-scarce language.
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A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Ruoming Pang
Antoine Bruguier
Wei Li
Raziel Alvarez
Chung-Cheng Chiu
David Garcia
Kevin Hu
Minho Jin
Qiao Liang
Cal Peyser
David Rybach
(June) Yuan Shangguan
Yash Sheth
Mirkó Visontai
Yu Zhang
Ding Zhao
ICASSP (2020)
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Thus far, end-to-end (E2E) models have not shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In this paper, we develop a first-pass Recurrent Neural Network Transducer (RNN-T) model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency. On the quality side, we incorporate a large number of utterances across varied domains to increase acoustic diversity and the vocabulary seen by the model. We also train with accented English speech to make the model more robust to different pronunciations. In addition, given the increased amount of training data, we explore a varied learning rate schedule. On the latency front, we explore using the end-of-sentence decision emitted by the RNN-T model to close the microphone, and also introduce various optimizations to improve the speed of LAS rescoring. Overall, we find that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional model. For example, for the same latency, RNN-T+LAS obtains a 8% relative improvement in WER, while being more than 400-times smaller in model size.
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On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
Kazuki Irie
Antoine Bruguier
David Rybach
Patrick Nguyen
Interspeech (2019)
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In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various target units (phoneme, grapheme, and word-piece); across all tasks, we find that grapheme or word-piece models consistently outperform phoneme-based models, even though they are evaluated without a lexicon or an external language model. We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model. Rescoring an N-best list generated by the phonemic system, however, provides limited improvements. Further analysis shows that the word-piece-based models produce more diverse N-best hypotheses, and thus lower oracle WERs, than phonemic models.
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Extracting Symptoms and their Status from Clinical Conversations
Nan Du
Linh Tran
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy (2019), pp. 915-9125
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This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly available corpus in this privacy-sensitive domain led us to develop our own corpus, consisting of about 3K conversations annotated by professional medical scribes. We propose two novel deep learning approaches to infer the symptom names and their status: (1) a new hierarchical span-attribute tagging (SAT) model, trained using curriculum learning, and (2) a variant of sequence-to-sequence model which decodes the symptoms and their status from a few speaker turns within a sliding window over the conversation. This task stems from a realistic application of assisting medical providers in capturing symptoms mentioned by patients from their clinical conversations. To reflect this application, we define multiple metrics. From inter-rater agreement, we find that the task is inherently difficult. We conduct comprehensive evaluations on several contrasting conditions and observe that the performance of the models range from an F-score of 0.5 to 0.8 depending on the condition. Our analysis not only reveals the inherent challenges of the task, but also provides useful directions to improve the models.
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STREAMING END-TO-END SPEECH RECOGNITION FOR MOBILE DEVICES
Raziel Alvarez
Ding Zhao
David Rybach
Ruoming Pang
Qiao Liang
Deepti Bhatia
Yuan Shangguan
ICASSP (2019)
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End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present numerous challenges: In order to be truly useful, such models must decode speech utterances in a streaming fashion, in real time; they must be robust to the long tail of use cases; they must be able to leverage user-specific context (e.g., contact lists); and above all, they must be extremely accurate. In this work, we describe our efforts at building an E2E speech recognizer using a recurrent neural network transducer. In experimental evaluations, we find that the proposed approach can outperform a conventional CTC-based model in terms of both latency and accuracy in a number of evaluation categories.
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Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Arindrima Datta
Interspeech 2019 (2019) (to appear)
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Multilingual end-to-end (E2E) models have shown great
promise as a means to expand coverage of the world’s lan-
guages by automatic speech recognition systems. They im-
prove over monolingual E2E systems, especially on low re-
source languages, and simplify training and serving by elimi-
nating language-specific acoustic, pronunciation, and language
models. This work aims to develop an E2E multilingual system
which is equipped to operate in low-latency interactive applica-
tions as well as handle the challenges of real world imbalanced
data. First, we present a streaming E2E multilingual model.
Second, we compare techniques to deal with imbalance across
languages. We find that a combination of conditioning on a
language vector and training language-specific adapter layers
produces the best model. The resulting E2E multilingual model
system achieves lower word error rate (WER) than state-of-the-
art conventional monolingual models by at least 10% relative
on every language.
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Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning
Alvin Rishi Rajkomar
Kat Chou
Journal of the American Medical Association (2019)
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Introduction: Auto-charting -- creation structured sections of clinical notes generated directly from a patient-doctor encounter -- holds promise to lift documentation burden from physicians. However, clinicians exercise professional judgement in what and how to document, and it is unknown if a machine learning (ML) model could assist with these tasks.
Objective: Build a ML model to extract symptoms and status (i.e. experienced, not-experienced, not relevant for note) from transcripts of patient-doctor encounters and assess performance on common symptoms and conversations in which a human interpreterscribe is not used.
Methods: We generated a ML model to auto-generate a review of systems (ROS) from transcripts of 90,000 de-identified medical encounters. 2950 transcripts were labeled by medical scribes to identify 171 common symptoms. Model accuracy was stratified by how clearly a symptom was mentioned in conversation for 800 snippets, which was assessed by a formal rating system termed conversational clarity. The model was also qualitatively assessed in a variety of conversational motifs.
Results: Overall, the model had a sensitivity of 0.71 of matching the exact symptom labeled by a human with a positive predictive value of 0.69. Model sensitivity was associated with the clarity of a conversational (p<0.0001). 39.5% (316/800) snippets of common symptoms contained symptoms mentioned with high clarity, and in this group, the sensitivity of the model was 0.91. The model was robust to a variety of conversational motifs (e.g. detecting symptoms mentioned in colloquial ways).
Conclusions: Auto-generating a review of systems is feasible across a wide-range symptoms that are commonly discussed in doctor-patient encounter
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Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
Patrick Nguyen
Chung-Cheng Chiu
ICASSP 2018 (to appear)
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Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds to improving the log-likelihood of the data. However, system performance is usually measured in terms of word error rate (WER), not log-likelihood. Traditional ASR systems benefit from discriminative sequence training which optimizes criteria such as the state-level minimum Bayes risk (sMBR) which are more closely related to WER. In the present work, we explore techniques to train attention-based models to directly minimize expected word error rate. We consider two loss functions which approximate the expected number of word errors: either by sampling from the model, or by using N-best lists of decoded hypotheses, which we find to be more effective than the sampling-based method. In experimental evaluations, we find that the proposed training procedure improves performance by up to 8.2% relative to the baseline system. This allows us to train grapheme-based, uni-directional attention-based models which match the performance of a traditional, state-of-the-art, discriminative sequence-trained system on a mobile voice-search task.
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State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Chung-Cheng Chiu
Patrick Nguyen
Katya Gonina
Navdeep Jaitly
Jan Chorowski
ICASSP (2018) (to appear)
Preview abstract
Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In our previous work, we have shown that such architectures are comparable to state-of-the-art ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore techniques such as synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12,500 hour voice search task, we find that the proposed changes improve the WER of the LAS system from 9.2% to 5.6%, while the best conventional system achieve 6.7% WER. We also test both models on a dictation dataset, and our model provide 4.1% WER while the conventional system provides 5% WER.
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