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
(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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Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
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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On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
Kazuki Irie
Antoine Bruguier
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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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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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
Ruoming Pang
Qiao Liang
Deepti Bhatia
Yuan Shangguan
ICASSP (2019)
Preview abstract
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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A comparison of techniques for language model integration in encoder-decoder speech recognition
Shubham Toshniwal
Chung-Cheng Chiu
Karen Livescu
IEEE SLT (2018)
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Attention-based recurrent neural encoder-decoder models present an
elegant solution to the automatic speech recognition problem. This
approach folds the acoustic model, pronunciation model, and language
model into a single network and requires only a parallel corpus
of speech and text for training. However, unlike in conventional
approaches that combine separate acoustic and language models, it
is not clear how to use additional (unpaired) text. While there has
been previous work on methods addressing this problem, a thorough
comparison among methods is still lacking. In this paper, we compare
a suite of past methods and some of our own proposed methods
for using unpaired text data to improve encoder-decoder models. For
evaluation, we use the medium-sized Switchboard data set and the
large-scale Google voice search and dictation data sets. Our results
confirm the benefits of using unpaired text across a range of methods
and data sets. Surprisingly, for first-pass decoding, the rather simple
approach of shallow fusion performs best across data sets. However,
for Google data sets we find that cold fusion has a lower oracle error
rate and outperforms other approaches after second-pass rescoring
on the Google voice search data set.
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Speech recognition for medical conversations
Chung-Cheng Chiu
Kat Chou
Chris Co
Navdeep Jaitly
Diana Jaunzeikare
Patrick Nguyen
Ananth Sankar
Justin Jesada Tansuwan
Nathan Wan
Frank Zhang
Interspeech 2018 (2018)
Preview abstract
In this paper we document our experiences with developing speech recognition for Medical Transcription -- a system that automatically transcribes notes from doctor-patient conversations. Towards this goal, we built a system along two different methodological lines -- a Connectionist Temporal Classification (CTC) phoneme based model and a Listen Attend and Spell (LAS) model. To train these models we used a corpus of anonymized conversations representing approximately 14,000 hours of speech . Because of noisy transcripts and alignments in the corpus, a significant amount of effort was invested in data cleaning issues. We describe a two-stage strategy we followed for segmenting the data. The data cleanup and development of a matched language model was essential to the success of the CTC based models. The LAS based models, however were found to be resilient to alignment and transcript noise and did not require the use of language models. CTC models were able to achieve a word error rate of 20.1%, and the LAS models were able to achieve 18.5%.
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No Need For A Lexicon? Evaluating The Value Of The Pronunciation Lexica In End-To-End Models
Seungji Lee
Vlad Schogol
Patrick Nguyen
Chung-Cheng Chiu
ICASSP (2018)
Preview abstract
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-to-end models which seek to combine acoustic, pronunciation, and language model components into a single neural network. Such systems, which typically predict graphemes or words, simplify the recognition process since they remove the need for a separate expert-curated pronunciation lexicon to map from phoneme-based units to words. However, there has been little previous work comparing phoneme-based versus grapheme-based sub-word units in the end-to-end modeling framework, to determine whether the gains from such approaches are primarily due to the new probabilistic model, or from the joint learning of the various components with grapheme-based units.
In this work, we conduct detailed experiments which are aimed at quantifying the value of phoneme-based pronunciation lexica in the context of end-to-end models. We examine phoneme-based end-to-end models, which are contrasted against grapheme-based ones on a large vocabulary English Voice-search task, where we find that graphemes do indeed outperform phoneme-based models. We also compare grapheme and phoneme-based end-to-end approaches on a multi-dialect English task, which once again confirm the superiority of graphemes, greatly simplifying the system for recognizing multiple
dialects.
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In this work we present a method for semi-supervised learning
from transcripts of dialogue between humans. We consider
the scenario in which a large amount of transcripts are available,
and we would like to extract some semantic information from
them; however, only a small number of transcripts have been
labeled with this information. We present a method for leveraging
the unlabeled data to learn a better model than could be
learned from the labeled data alone. First, a recurrent neural
network (RNN) encoder-decoder is trained on the task of predicting
nearby turns on the full dialogue corpus; next, the RNN
encoder is reused as a feature representation for the supervised
learning problem. While previous work has explored the use of
pre-training for non-dialogue corpora, our method is specifically
geared toward the dialogue use case. We demonstrate an improvement
on a clinical documentation task, particularly in the
regime of small amounts of labeled data. We compare several
types of encoders, both in the context of a classification task and
in a human-evaluation of their learned representations. We show
that our method significantly improves the classification task in
the case where only a small amount of labeled data is available.
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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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Improving the Performance of Online Neural Transducer models
Chung-Cheng Chiu
Patrick Nguyen
Proc. ICASSP (2018)
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Having an sequence-to-sequence model which can operate in an online fashion is important for streaming applications such as Voice Search. Neural transducer is a streaming sequence-to-sequence model, but has shown to degrade significantly in performance compared to non-streaming models such as Listen, Attend and Spell (LAS). In this paper, we present various improvements to NT. Specifically, we look at increasing the window over which NT computes attention, mainly by looking backwards in time so the model still remains online. In addition, we explore initializing a NT model from a LAS-trained model so that it is guided with a better alignment. Finally. we explore including stronger language models such as using wordpiece models, and applying an external LM during the beam search. On a Voice Search task, we find with these improvements we can get NT to match the performance of LAS.
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Recent work has shown that end-to-end (E2E) speech
recognition architectures such as Listen Attend and Spell (LAS)
can achieve state-of-the-art quality results in LVCSR tasks. One
benefit of this architecture is that it does not require a separately
trained pronunciation model, language model, and acoustic
model. However, this property also introduces a drawback:
it is not possible to adjust language model contributions separately
from the system as a whole. As a result, inclusion of
dynamic, contextual information (such as nearby restaurants or
upcoming events) into recognition requires a different approach
from what has been applied in conventional systems.
We introduce a technique to adapt the inference process
to take advantage of contextual signals by adjusting the output
likelihoods of the neural network at each step in the beam
search. We apply the proposed method to a LAS E2E model
and show its effectiveness in experiments on a voice search task
with both artificial and real contextual information. Given optimal
context, our system reduces WER from 9.2% to 3.8%.
The results show that this technique is effective at incorporating
context into the prediction of an E2E system.
Index Terms: speech recognition, end-to-end, contextual
speech recognition, neural network
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An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model
Patrick Nguyen
ICASSP (2018)
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Attention-based sequence-to-sequence models for automatic
speech recognition jointly train an acoustic model, language model,
and alignment mechanism. Thus, the language model component is
only trained on transcribed audio-text pairs. This leads to the use of
shallow fusion with an external language model at inference time.
Shallow fusion refers to log-linear interpolation with a separately
trained language model at each step of the beam search. In this
work, we investigate the behavior of shallow fusion across a range of
conditions: different types of language models, different decoding
units, and different tasks. On Google Voice Search, we demonstrate
that the use of shallow fusion with an neural LM with wordpieces
yields a 9.1% relative word error rate reduction (WERR) over our
competitive attention-based sequence-to-sequence model, obviating
the need for second-pass rescoring.
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In automatic speech recognition (ASR) what a user says
depends on the particular context she is in. Typically, this
context is represented as a set of word n-grams. In this work,
we present a novel, all-neural, end-to-end (E2E) ASR system
that utilizes such context. Our approach, which we refer
to as Contextual Listen, Attend and Spell (CLAS) jointlyoptimizes
the ASR components along with embeddings of the
context n-grams. During inference, the CLAS system can be
presented with context phrases which might contain out-ofvocabulary
(OOV) terms not seen during training. We compare
our proposed system to a more traditional contextualization
approach, which performs shallow-fusion between independently
trained LAS and contextual n-gram models during
beam search. Across a number of tasks, we find that the proposed
CLAS system outperforms the baseline method by as
much as 68% relative WER, indicating the advantage of joint
optimization over individually trained components.
Index Terms: speech recognition, sequence-to-sequence
models, listen attend and spell, LAS, attention, embedded
speech recognition.
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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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Smart Reply: Automated Response Suggestion for Email
Karol Kurach
Sujith Ravi
Tobias Kaufman
Laszlo Lukacs
Peter Young
Vivek Ramavajjala
Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2016).
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In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete email responses with just one tap on mobile. The system is currently used in Inbox by Gmail and is responsible for assisting with 10% of all mobile responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning.
We describe the architecture of the system as well as the challenges that we faced while building it, like response diversity and scalability. We also introduce a new method for semantic clustering of user-generated content that requires only a modest amount of explicitly labeled data.
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The recent application of RNN encoder-decoder models has resulted in substantial progress in fully data-driven dialogue systems, but evaluation remains a challenge. An adversarial loss could be a way to directly evaluate the extent to which generated dialogue responses sound like they came from a human. This could reduce the need for human evaluation, while more directly evaluating on a generative task. In this work, we investigate this idea by training an RNN to discriminate a dialogue model's samples from human-generated samples. Although we find some evidence this setup could be viable, we also note that many issues remain in its practical application. We discuss both aspects and conclude that future work is warranted.
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