The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing , and machine learning.

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CHIME Dataset

A unified version of the CHIME dataset created from the following resources:

Paper Code 🤗 parent-child relations 🤗 siblings info 🤗 claim-category relations

  Overview

The CHIME Dataset is a unified collection of data specifically designed for evaluating the capabilities of Large Language Models (LLMs) in generating literature reviews. It includes various relationships between academic papers, such as parent-child relations, sibling information, and… See the full description on the dataset page: https://huggingface.co/datasets/nhop/chime.

Kinect-WSJ is a multichannel, multispeaker, reverberated, noisy dataset which extends the WSJ0-2mix singlechannel, non-reverberated, noiseless dataset to the strong reverberation and noise conditions and the Kinect-like microphone array geometry used in CHiME-5.

We introduce ChannelSet, a dataset which provides a launchpad for exploring the extraneous acoustic information typically suppressed or ignored in audio tasks such as automatic speech recognition. We combined components of existing publicly available datasets to encompass broad variability in recording equipment, microphone position, room or surrounding acoustics, event density (i.e., how many audio events are present), and proportion of foreground and background sounds. Source datasets include: the CHiME-3 background dataset, CHiME-5 evaluation dataset, AMI meeting corpus, Freefield1010, and Vystadial2016.

ChannelSet includes 13 classes spanning various acoustic environments: Indoor_Commercial_Bus, Indoor_Commercial_Cafe, Indoor_Domestic, Indoor_Meeting_Room1, Indoor_Meeting_Room2, Indoor_Meeting_Room3, Outdoor_City_Pedestrian, Outdoor_City_Traffic, Outdoor_Nature_Birds, Outdoor_Nature_Water, Outdoor_Nature_Weather, Telephony_CZ, and Telephony_EN. Each sample is between 1 and 10 seconds in duration. Each class contains 100 minutes of audio, for a total of 21.6 hours, split into separate test (20%) and train (80%) partitions.

Download includes scripts, metadata, and instructions for producing ChannelSet from source datasets.

Please note, the updated version of this toolbox is now available for download on this page. The COVID-19-Modeling-v1.zip file contains version 5 of the toolbox with updated documentation. Version 5 of the toolbox updates the CHIME Model v1.1.5 tool. The COVID-19Surge (CDC) model is unchanged in this version.More information about the toolbox can be found in the toolbox document. More information about the CHIME Model v1.1.5 tool, including the change log, can be found in the tool documentation and this video.More information about the COVID-19Surge (CDC) tool is included in the tool documentation and this video. CHIME Model v1.1.5 ToolVersion 4 - Updated 11 MAY 2020An implementation of Penn Medicine’s COVID-19 Hospital Impact Model for Epidemics (CHIME) for use in ArcGIS Pro 2.3 or later. This tool leverages SIR (Susceptible, Infected, Recovered) modeling to assist hospitals, cities, and regions with capacity planning around COVID-19 by providing estimates of daily new admissions and current inpatient hospitalizations (census), ICU admissions, and patients requiring ventilation. Version 4 of this tool is based on CHIME v1.1.5 (2020-05-07). Learn more about how CHIME works.Version 4 contains the following updates:Updated the CHIME tool from CHIME v1.1.2 to CHIME v1.1.5.Added a new parameter called Date of Social Distancing Measures Effect to specify the date when social distancing measures started showing their effects.Added a new parameter called Recovery to specify the number of recovered cases at the start of the model.COVID-19Surge (CDC) ToolVersion 1 - Released 04 MAY 2020An implementation of Centers for Disease Control and Prevention’s (CDC) COVID-19Surge for use in ArcGIS Pro 2.3 or later. This tool leverages SIICR (Susceptible, Infected, Infectious, Convalescing, Recovered) modeling to assist hospitals, cities, and regions with capacity planning around COVID-19 by providing estimates of daily new admissions and current inpatient hospitalizations (census), ICU admissions, and patients requiring ventilation based on the extent to which mitigation strategies such as social distancing or shelter-in-place recommendations are implemented. This tool is based on COVID-19Surge. Learn more about how COVID-19Surge works.Potential ApplicationsThe illustration above depicts the outputs of the COVID-19Surge (CDC) tool of the COVID-19 Modeling toolbox.A hospital systems administrator needs a simple model to project the number of patients the hospitals in the network will need to accommodate in the next 90 days due to COVID-19. You know the population served by each hospital, the date and level of current social distancing, the number of people who have recovered, and the number of patients that are currently hospitalized with COVID-19 in each facility. Using your hospital point layer, you run the CHIME Model v1.1.5 tool.An aid agency wants to estimate where and when resources will be required in the counties you serve. You know the population and number of COVID-19 cases today and 14 days ago in each county. You run the COVID-19Surge (CDC) tool using your county polygon data, introducing an Intervention Policy and New Infections Per Case (R0) driven by fields to account for differences in anticipated social distancing policies and effectiveness between counties.A county wants to understand how the lessening or removal of interventions may impact hospital bed availability within the county. You run the CHIME Model v1.1.5 and COVID-19Surge (CDC) tool, checking Add Additional Web App Fields in Summary in both tools. You display the published results from each tool in the Capacity Analysis configurable app so estimates can be compared between models.This toolbox requires any license of ArcGIS Pro 2.3 or higher in order to run. Steps for upgrading ArcGIS Pro can be found here.For questions, comments and support, please visit our COVID-19 GeoNet community.

AbstractIntroduction Second DIHARD Challenge Evaluation - Eleven Sources was developed by the Linguistic Data Consortium (LDC) and contains approximately 20 hours of English and Chinese speech data along with corresponding annotations used in support of the Second DIHARD Challenge. The DIHARD Challenges are a set of shared tasks on diarization focusing on "hard" diarization; that is, speech diarization for challenging corpora where there was an expectation that existing state-of-the-art systems would fare poorly. As with the first challenge, the second development and evaluation sets were drawn from a diverse sampling of sources including monologues, map task dialogues, broadcast interviews, sociolinguistic interviews, meeting speech, speech in restaurants, clinical recordings, extended child language acquisition recordings, and YouTube videos. Data This release, when combined with Second DIHARD Challenge Evaluation - SEEDLingS (forthcoming from LDC), contains the evaluation set audio data and annotation, except for CHiME-5 audio files, which must be obtained from the University of Sheffield. Data sources in this release are as follows (all sources are in English unless otherwise indicated): Autism Diagnosis Observation Schedule (ADOS) interviews Conversations in Restaurants DCIEM/HCRC map task (LDC96S38) Audiobook recordings from LibriVox Meeting speech from 2004 Spring NIST Rich Transcription (RT-04S) Development (LDC2007S11) and Evaluation (LDC2007S12) releases Meeting speech collected by LDC in 2001 for the ROAR project (see, e.g., ISL Meeting Speech Part 1 (LDC2004S05)) 2001 U.S. Supreme Court oral arguments Sociolinguistic interviews from SLX Corpus of Classic Sociolinguistic Interviews (LDC2003T15) Mixer 6 Speech (LDC2013S03) English and Chinese video collected by LDC as part of the Video Annotation for Speech Technologies (VAST) project YouthPoint radio interviews All audio is provided in the form of 16 kHz, 16-bit, mono-channel FLAC files. The diarization for each recording is stored as a NIST Rich Transcription Time Marked (RTTM) file. RTTM files are space-separated text files containing one turn per line. Segmentation files are stored as HTK label files. Each of these files contains one speech segment per line. Scoring regions for each recording are specific by un-partitioned evaluation map (UEM) files. All annotation file types are encoded as UTF-8. More information about file formats, data sources and domains is contained in the included documentation.

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AbstractIntroduction Second DIHARD Challenge Development - SEEDLinGS was developed by Duke University and LDC and contains approximately two hours of English child language recordings along with corresponding annotations used in support of the Second DIHARD Challenge. This release, when combined with Second DIHARD Challenge Development - Eleven Sources (LDC2021S10), contains the development set audio data and annotation, except for CHiME-5 audio files, which must be obtained from the University of Sheffield. The DIHARD Challenges are a set of shared tasks on diarization focusing on "hard" diarization; that is, speech diarization for challenging corpora where there was an expectation that existing state-of-the-art systems would fare poorly. As with the first challenge, the second development and evaluation sets were drawn from a diverse sampling of sources including monologues, map task dialogues, broadcast interviews, sociolinguistic interviews, meeting speech, speech in restaurants, clinical recordings, extended child language acquisition recordings, and YouTube videos. Data Source data is from the SEEDLingS (The Study of Environmental Effects on Developing Linguistic Skills) corpus, designed to investigate how infants' early linguistic and environmental input plays a role in their learning. Recordings were generated in the home environment of infants in the Rochester, New York area. A subset of that data was annotated by LDC for use in the First and Second DIHARD Challenges. The data in this release consists of files provided in the Second DIHARD Challenge as well as subsequently updated annotated files not provided to second challenge participants. All audio is provided in the form of 16 kHz, 16-bit, mono-channel FLAC files. The diarization for each recording is stored as a NIST Rich Transcription Time Marked (RTTM) file. RTTM files are space-separated text files containing one turn per line. Segmentation files are stored as HTK label files. Each of these files contains one speech segment per line. Scoring regions for each recording are specific by un-partitioned evaluation map (UEM) files. All annotation file types are encoded as UTF-8. More information about the file formats and data sources and domains are in the included documentation.

AbstractIntroduction Second DIHARD Challenge Evaluation - SEEDLingS was developed by Duke University and the Linguistic Data Consortium (LDC) and contains approximately two hours of English child language recordings along with corresponding annotations used in support of the Second DIHARD Challenge. The DIHARD Challenges are a set of shared tasks on diarization focusing on "hard" diarization; that is, speech diarization for challenging corpora where there was an expectation that existing state-of-the-art systems would fare poorly. As with the first challenge, the second development and evaluation sets were drawn from a diverse sampling of sources including monologues, map task dialogues, broadcast interviews, sociolinguistic interviews, meeting speech, speech in restaurants, clinical recordings, extended child language acquisition recordings, and web videos. Data Source data is from the SEEDLingS (The Study of Environmental Effects on Developing Linguistic Skills) corpus, designed to investigate how infants' early linguistic and environmental input plays a role in their learning. Recordings were generated in the home environment of infants in the Rochester, New York area. A subset of that data was annotated by LDC for use in the First and Second DIHARD Challenges. This release, when combined with Second DIHARD Challenge Evaluation - Eleven Sources (LDC2022S06), contains the evaluation set audio data and annotation, except for CHiME-5 audio files which must be obtained from the University of Sheffield. All audio is provided in the form of 16 kHz, 16-bit, mono-channel FLAC files. The diarization for each recording is stored as a NIST Rich Transcription Time Marked (RTTM) file. RTTM files are space-separated text files containing one turn per line. Segmentation files are stored as HTK label files. Each of these files contains one speech segment per line. Scoring regions for each recording are specific by un-partitioned evaluation map (UEM) files. All annotation file types are encoded as UTF-8. More information about file formats, data sources and domains is contained in the included documentation.