The WeCanTalk (WCT) Corpus is a new multi-language, multi-modal resource for speaker recognition. The corpus contains Cantonese, Mandarin and English telephony and video speech data from over 200 multilingual speakers located in Hong Kong. Each speaker contributed at least 10 telephone conversations of 8-10 minutes’ duration collected via a custom telephone platform based in Hong Kong. Speakers also uploaded at least 3 videos in which they were both speaking and visible, along with one selfie image. At least half of the calls and videos for each speaker were in Cantonese, while their remaining recordings featured one or more different languages. Both calls and videos were made in a variety of noise conditions. All speech and video recordings were audited by experienced multilingual annotators for quality including presence of the expected language and for speaker identity. The WeCanTalk Corpus has been used to support the NIST 2021 Speaker Recognition Evaluation and will be published in the LDC catalog.

We introduce a new resource, the SAFE-T (Speech Analysis for Emergency Response Technology) Corpus, designed to simulate first-responder communications by inducing high vocal effort and urgent speech with situational background noise in a game-based collection protocol. Linguistic Data Consortium developed the SAFE-T Corpus to support the NIST (National Institute of Standards and Technology) OpenSAT (Speech Analytic Technologies) evaluation series, whose goal is to advance speech analytic technologies including automatic speech recognition, speech activity detection and keyword search in multiple domains including simulated public safety communications data. The corpus comprises over 300 hours of audio from 115 unique speakers engaged in a collaborative problem-solving activity representative of public safety communications in terms of speech content, noise types and noise levels. Portions of the corpus have been used in the OpenSAT 2019 evaluation and the full corpus will be published in the LDC catalog. We describe the design and implementation of the SAFE-T Corpus collection, discuss the approach of capturing spontaneous speech from study participants through game-based speech collection, and report on the collection results including several challenges associated with the collection.

We introduce the Call My Net 2 (CMN2) Corpus, a new resource for speaker recognition featuring Tunisian Arabic conversations between friends and family, incorporating both traditional telephony and VoIP data. The corpus contains data from over 400 Tunisian Arabic speakers collected via a custom-built platform deployed in Tunis, with each speaker making 10 or more calls each lasting up to 10 minutes. Calls include speech in various realistic and natural acoustic settings, both noisy and non-noisy. Speakers used a variety of handsets, including landline and mobile devices, and made VoIP calls from tablets or computers. All calls were subject to a series of manual and automatic quality checks, including speech duration, audio quality, language identity and speaker identity. The CMN2 corpus has been used in two NIST Speaker Recognition Evaluations (SRE18 and SRE19), and the SRE test sets as well as the full CMN2 corpus will be published in the Linguistic Data Consortium Catalog. We describe CMN2 corpus requirements, the telephone collection platform, and procedures for call collection. We review properties of the CMN2 dataset and discuss features of the corpus that distinguish it from prior SRE collection efforts, including some of the technical challenges encountered with collecting VoIP data.

The Multi-language Speech (MLS) Corpus supports NIST’s Language Recognition Evaluation series by providing new conversational telephone speech and broadcast narrowband data in 20 languages/dialects. The corpus was built with the intention of testing system performance in the matter of distinguishing closely related or confusable linguistic varieties, and careful manual auditing of collected data was an important aspect of this work. This paper lists the specific data requirements for the collection and provides both a commentary on the rationale for those requirements as well as an outline of the various steps taken to ensure all goals were met as specified. LDC conducted a large-scale recruitment effort involving the implementation of candidate assessment and interview techniques suitable for hiring a large contingent of telecommuting workers, and this recruitment effort is discussed in detail. We also describe the telephone and broadcast collection infrastructure and protocols, and provide details of the steps taken to pre-process collected data prior to auditing. Finally, annotation training, procedures and outcomes are presented in detail.

The DARPA RATS program was established to foster development of language technology systems that can perform well on speaker-to-speaker communications over radio channels that evince a wide range in the type and extent of signal variability and acoustic degradation. Creating suitable corpora to address this need poses an equally wide range of challenges for the collection, annotation and quality assessment of relevant data. This paper describes the LDC’s multi-year effort to build the RATS data collection, summarizes the content and properties of the resulting corpora, and discusses the novel problems and approaches involved in ensuring that the data would satisfy its intended use, to provide speech recordings and annotations for training and evaluating HLT systems that perform 4 specific tasks on difficult radio channels: Speech Activity Detection (SAD), Language Identification (LID), Speaker Identification (SID) and Keyword Spotting (KWS).