Discriminating Groups

A group G is termed discriminating if every group separated by G is discriminated by G. In this paper we answer several questions concerning discrimination which arose from [2]. We prove that a finitely generated equationally Noetherian group G is discriminating if and only if the quasivariety generated by G is the minimal universal class containing G. Among other results, we show that the non-abelian free nilpotent groups are non-discriminating. Finally we list some open problems concerning discriminating groups

Clarity-2021 challenges : machine learning challenges for advancing hearing aid processing

In recent years, rapid advances in speech technology have been made possible by machine learning challenges such as CHiME, REVERB, Blizzard, and Hurricane. In the Clarity project, the machine learning approach is applied to the problem of hearing aid processing of speech-in-noise, where current technology in enhancing the speech signal for the hearing aid wearer is often ineffective. The scenario is a (simulated) cuboid-shaped living room in which there is a single listener, a single target speaker and a single interferer, which is either a competing talker or domestic noise. All sources are static, the target is always within ±30◦ azimuth of the listener and at the same elevation, and the interferer is an omnidirectional point source at the same elevation. The target speech comes from an open source 40- speaker British English speech database collected for this purpose. This paper provides a baseline description of the round one Clarity challenges for both enhancement (CEC1) and prediction (CPC1). To the authors’ knowledge, these are the first machine learning challenges to consider the problem of hearing aid speech signal processin

Dataset of British English speech recordings for psychoacoustics and speech processing research: The clarity speech corpus

This paper presents the Clarity Speech Corpus, a publicly available, 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 transcripts and metadata

Speech produced in noise: Relationship between listening difficulty and acoustic and durational parameters.

Conversational speech produced in noise can be characterised by increases in intelligibility relative to such speech produced in quiet. Listening difficulty (LD) is a metric that can be used to evaluate speech transmission performance more sensitively than intelligibility scores in situations in which performance is likely to be high. The objectives of the present study were to evaluate the LD of speech produced in different noise and style conditions, to evaluate the spectral and durational speech modifications associated with these conditions, and to determine whether any of the spectral and durational parameters predicted LD. Nineteen subjects were instructed to speak at normal and loud volumes in the presence of background noise at 40.5 dB(A) and babble noise at 61 dB(A). The speech signals were amplitude-normalised, combined with pink noise to obtain a signal-to-noise ratio of -6 dB, and presented to twenty raters who judged their LD. Vowel duration, fundamental frequency and the proportion of the spectral energy in high vs low frequencies increased with the noise level within both styles. LD was lowest when the speech was produced in the presence of high level noise and at a loud volume, indicating improved intelligibility. Spectrum balance was observed to predict LD