WER we are and WER we think we are

Szymański, P.; Żelasko, Piotr; Morzy, Mikołaj; Szymczak, Adrian; Żyła-Hoppe, Marzena; Banaszczak, Joanna; Augustyniak, Lukasz; Mizgajski, J.; Carmiel, Yishay

doi:10.48550/arxiv.2010.03432

Cited by 2 publications

(1 citation statement)

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“…Modern ASR systems can accurately transcribe pre-segmented utterances recorded in acoustically moderate environments, as demonstrated in many ASR benchmarks such as Lib-riSpeech [1] and Switchboard [2], and are widely used in our daily lives. Yet, there are many challenges that must be overcome for the ASR technology to become usable more broadly [3,4]. One of the unsolved challenges is transcribing unsegmented natural conversations, where multiple people talk over each other in a spontaneous and thus unpredictable way.…”

Section: Introductionmentioning

confidence: 99%

VarArray: Array-Geometry-Agnostic Continuous Speech Separation

Yoshioka¹,

Wang²,

Wang³

et al. 2021

Preprint

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Continuous speech separation using a microphone array was shown to be promising in dealing with the speech overlap problem in natural conversation transcription. This paper proposes VarArray, an arraygeometry-agnostic speech separation neural network model. The proposed model is applicable to any number of microphones without retraining while leveraging the nonlinear correlation between the input channels. The proposed method adapts different elements that were proposed before separately, including transform-averageconcatenate, conformer speech separation, and inter-channel phase differences, and combines them in an efficient and cohesive way. Large-scale evaluation was performed with two real meeting transcription tasks by using a fully developed transcription system requiring no prior knowledge such as reference segmentations, which allowed us to measure the impact that the continuous speech separation system could have in realistic settings. The proposed model outperformed a previous approach to array-geometry-agnostic modeling for all of the geometry configurations considered, achieving asclite-based speaker-agnostic word error rates of 17.5% and 20.4% for the AMI development and evaluation sets, respectively, in the end-to-end setting using no ground-truth segmentations.

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Section: Introductionmentioning

confidence: 99%

VarArray: Array-Geometry-Agnostic Continuous Speech Separation

Yoshioka¹,

Wang²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

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Speech recognition technology in radiology

Kudryavtsev,

Bardasova,

Khoruzhaya

2023

Digital Diagnostics

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Speech recognition technology is a promising tool for healthcare systems. This technology has a fairly long history of use in US and European healthcare systems, beginning in the 1970s. However, it only became widespread at the beginning of the 21st century, replacing medical transcriptionists. For Russian healthcare system, speech recognition technology is a new tool. Its active development began only in the early 2010s, and its active implementation in healthcare began in the late 2010s. Such a delay is due to the peculiarities of the Russian language and the limitation of computing power present at the beginning of the XXI century. Currently, speech recognition technology is used to fill out medical reports by voice, it allows you to reduce the preparation time of the protocol of radiological studies in comparison with the traditional (keyboard) text input. The review described a brief history of the development and application of speech recognition technology in radiology. Key scientific studies confirming the effectiveness of its use in US and European healthcare systems are described. Our experience in the use of speech recognition technology is demonstrated and its effectiveness is evaluated. The prospects for further development and application of this technology in Russian health care are described.

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WER we are and WER we think we are

Cited by 2 publications

References 0 publications

VarArray: Array-Geometry-Agnostic Continuous Speech Separation

VarArray: Array-Geometry-Agnostic Continuous Speech Separation

Speech recognition technology in radiology

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