Challenges to Revolutionise Hearing Device Processing

Lead Research Organisation: University of Salford
Department Name: Sch of Science,Engineering & Environment

Abstract

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Description Organised the first open Machine Learning Challenge for enhancing the processing of hearing aids in 2021. This was part of developing a new research area and growing the number of researchers considering hearing loss and speech technology. Non-academic entrants were from: Music Tribe; Google and Tancent. Academic entrants were: Brno University of Technology; ELO-SPHERES Consortium; University of Oldenburg; University of Sheffield; University Hannover and Shenzhen University. The results were presented at the The Clarity Workshop on Machine Learning Challenges for Hearing Aids (September 2021). Organised the first open Speech Intelligibility Prediction Challenge for people with a hearing loss listening via hearing aid. Ten entrants came from a variety of universities in Taiwan, China, Japan, Germany and UK. The results were presented at the The 2nd Clarity Workshop on Machine Learning Challenges for Hearing Aids (June 2022) Organised the second open Machine Learning Challenge for enhancing the processing of hearing aids in 2022. The second challenge substantially increased the difficulty of the problem by including multiple interferers and head movement during the stimulus. This second challenge was also used as the basis for an ICASSP Signal Processing grand challenge in 2022/23. The ICASSP challenge also included a more ecologically-valid and independent evaluation set. There were 10 teams between these two challenges. The results were presented at the 2022 2nd Clarity Workshop on Machine Learning Challenges for Hearing Aids (December 2022) at at ICASSP. www.claritychallenge.org
First Year Of Impact 2021
Sector Digital/Communication/Information Technologies (including Software),Other
Impact Types Cultural
 
Title Dataset of British English speech recordings for psychoacoustics and speech processing research 
Description The Clarity Speech Corpus is a forty speaker British English speech dataset. The corpus was created for the purpose of running listening tests to gauge speech intelligibility and quality in the Clarity Project, which has the goal of advancing speech signal processing by hearing aids through a series of challenges. The dataset is suitable for machine learning and other uses in speech and hearing technology, acoustics and psychoacoustics. The data comprises recordings of approximately 10,000 sentences drawn from the British National Corpus (BNC) with suitable length, words and grammatical construction for speech intelligibility testing. The collection process involved the selection of a subset of BNC sentences, the recording of these produced by 40 British English speakers, and the processing of these recordings to create individual sentence recordings with associated prompts and metadata.clarity_utterances.v1_2.tar.gz contains all the recordings as .wav files, with the accompanying metadata such as text prompts in clarity_master.json. Further details are given in the readme.Sample_clarity_utterances.zip contains a sample of 10. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
Impact Has been used in the Clarity Machine Learning Challenges. Only just published, so too soon for other impacts, 
URL https://salford.figshare.com/articles/dataset/Dataset_of_British_English_speech_recordings_for_psych...
 
Title claritychallenge/clarity_CC: clarity_CC release v1.2 
Description Software to support the Clarity Enhancement and Prediction Challenges. Python tools are provided for • Mixing scenes to generate the training and development data • Running the baseline hearing aid processing using the Oldenburg/Hoertech open Master Hearing Aid (openMHA) software platform • Running signals through a python implementation of the Moore, Stone, Baer and Glasberg (MSBG) hearing loss model • Estimating intelligibility using the Modified Binaural Short-Time Objective Intelligibility measure (MBSTOI). This version includes • The first release of the code for supporting the recently opened 1st Clarity Prediction Challenge (CPC1) • Code for supporting the (now closed) 1st Clarity Enhancement Challenge (CEC1) 
Type Of Technology Software 
Year Produced 2021 
Impact Used in the Clarity Machine Learning Challenges. 
URL https://zenodo.org/record/4593856