Speech recognition for medical conversations

Chiu, Chung‐Cheng; Tripathi, Anshuman; Chou, Katherine; Co, Chris; Jaitly, Navdeep; Jaunzeikare, Diana; Kannan, Anjuli; Nguyen, Patrick; Sak, Haşim; Sankar, Ananth; Tansuwan, Justin; Wan, Nathan; Wu, Yonghui; Zhang, Xuedong

doi:10.48550/arxiv.1711.07274

Cited by 9 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurate meeting transcriptions are the one of the processing steps in a pipeline for several tasks like summarization, topic extraction, etc. Similarly, the same transcription system can be used in other domains such as healthcare [198]. Although this task was introduced by NIST in the Rich Transcription Evaluation series back in 2003 [180,188,199], the initial systems had very poor performance, and consequently commercialization of the technology was not possible.…”

Section: Meeting Transcriptionmentioning

confidence: 99%

A Review of Speaker Diarization: Recent Advances with Deep Learning

Park¹,

Kanda²,

Dimitriadis³

et al. 2021

Preprint

View full text Add to dashboard Cite

Speaker diarization is a task to label audio or video recordings with classes corresponding to speaker identity, or in short, a task to identify "who spoke when". In the early years, speaker diarization algorithms were developed for speech recognition on multispeaker audio recordings to enable speaker adaptive processing, but also gained its own value as a stand-alone application over time to provide speaker-specific meta information for downstream tasks such as audio retrieval. More recently, with the rise of deep learning technology that has been a driving force to revolutionary changes in research and practices across speech application domains in the past decade, more rapid advancements have been made for speaker diarization. In this paper, we review not only the historical development of speaker diarization technology but also the recent advancements in neural speaker diarization approaches. We also discuss how speaker diarization systems have been integrated with speech recognition applications and how the recent surge of deep learning is leading the way of jointly modeling these two components to be complementary to each other. By considering such exciting technical trends, we believe that it is a valuable contribution to the community to provide a survey work by consolidating the recent developments with neural methods and thus facilitating further progress towards a more efficient speaker diarization.

show abstract

Section: Meeting Transcriptionmentioning

confidence: 99%

A Review of Speaker Diarization: Recent Advances with Deep Learning

Park¹,

Kanda²,

Dimitriadis³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Basically, SR-oriented and SS-oriented studies attempt to build automatic computer systems for interconversion between speech and text in the area of smart healthcare, making human-machine interaction as natural and flexible as human-human interaction [115]. For speech recognition, these efforts encompass the improvement in acoustic modelling [116], [117], language modelling [118], and the whole system pipeline [119], [120] to enhance recognition accuracy. For speech synthesis, recent advancements have been made in investigating and making synthesized speech natural [121], [122], intelligible [123]- [127] and expressive [128]- [130], which will help stimulate the enthusiasm of human-machine interaction [131].…”

Section: Nlp Approachmentioning

confidence: 99%

Natural Language Processing for Smart Healthcare

Zhou¹,

Yang²,

Shi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

show abstract

“…Importantly, we did not perform text normalization specific to each domain. In the domain of medical datasets, we use I2B2'14 (Stubbs and Uzuner, 2015), which consists of identified textual medical notes with PHI tagging, and the Audio Medical Conversations dataset from (Chiu et al, 2017), denoted AMC'17, which contains de-identified audio of doctor-patient conversations and their corresponding manual transcripts. Processing the AMC'17 conversations was facilitated by the fact that it is a de-identified dataset, which provides us with the locations of the PHI in the audio and the transcripts.…”

Section: Datasetsmentioning

confidence: 99%

“…Due to the rise of tele-medicine (Weinstein et al, 2014), clinical records consist of many other types of data, such as audio conversations (Chiu et al, 2017), scanned documents, video, and im-Figure 1: High level audio de-ID pipeline ages. In this work, we direct our attention towards the task of de-identifying clinical audio data.…”

Section: Introductionmentioning

confidence: 99%

Audio De-identification - a New Entity Recognition Task

Cohn¹,

Laish

Beryozkin

et al. 2019

Proceedings of the 2019 Conference of the North

View full text Add to dashboard Cite

Named Entity Recognition (NER) has been mostly studied in the context of written text. Specifically, NER is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. In such recordings, audio spans with personal information should be redacted, similar to the redaction of sensitive character spans in de-ID for written text. The application of NER in the context of audio de-identification has yet to be fully investigated. To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected. We then present our pipeline for this task, which involves Automatic Speech Recognition (ASR), NER on the transcript text, and text-to-audio alignment. Finally, we introduce a novel metric for audio de-ID and a new evaluation benchmark consisting of a large labeled segment of the Switchboard and Fisher audio datasets and detail our pipeline's results on it.

show abstract

Speech recognition for medical conversations

Cited by 9 publications

References 9 publications

A Review of Speaker Diarization: Recent Advances with Deep Learning

A Review of Speaker Diarization: Recent Advances with Deep Learning

Natural Language Processing for Smart Healthcare

Audio De-identification - a New Entity Recognition Task

Contact Info

Product

Resources

About