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

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

Combination of Spectral and Binaurally Created Harmonics in a Common Central Pitch Processor

A fundamental attribute of human hearing is the ability to extract a residue pitch from harmonic complex sounds such as those produced by musical instruments and the human voice. However, the neural mechanisms that underlie this processing are unclear, as are the locations of these mechanisms in the auditory pathway. The ability to extract a residue pitch corresponding to the fundamental frequency from individual harmonics, even when the fundamental component is absent, has been demonstrated separately for conventional pitches and for Huggins pitch (HP), a stimulus without monaural pitch information. HP is created by presenting the same wideband noise to both ears, except for a narrowband frequency region where the noise is decorrelated across the two ears. The present study investigated whether residue pitch can be derived by combining a component derived solely from binaural interaction (HP) with a spectral component for which no binaural processing is required. Fifteen listeners indicated which of two sequentially presented sounds was higher in pitch. Each sound consisted of two “harmonics,” which independently could be either a spectral or a HP component. Component frequencies were chosen such that the relative pitch judgement revealed whether a residue pitch was heard or not. The results showed that listeners were equally likely to perceive a residue pitch when one component was dichotic and the other was spectral as when the components were both spectral or both dichotic. This suggests that there exists a single mechanism for the derivation of residue pitch from binaurally created components and from spectral components, and that this mechanism operates at or after the level of the dorsal nucleus of the lateral lemniscus (brainstem) or the inferior colliculus (midbrain), which receive inputs from the medial superior olive where temporal information from the two ears is first combined

Spike-Timing-Based Computation in Sound Localization

Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination) in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal

Central auditory masking by an illusory tone

Many natural sounds fluctuate over time. The detectability of sounds in a sequence can be reduced by prior stimulation in a process known as forward masking. Forward masking is thought to reflect neural adaptation or neural persistence in the auditory nervous system, but it has been unclear where in the auditory pathway this processing occurs. To address this issue, the present study used a "Huggins pitch" stimulus, the perceptual effects of which depend on central auditory processing. Huggins pitch is an illusory tonal sensation produced when the same noise is presented to the two ears except for a narrow frequency band that is different (decorrelated) between the ears. The pitch sensation depends on the combination of the inputs to the two ears, a process that first occurs at the level of the superior olivary complex in the brainstem. Here it is shown that a Huggins pitch stimulus produces more forward masking in the frequency region of the decorrelation than a noise stimulus identical to the Huggins-pitch stimulus except with perfect correlation between the ears. This stimulus has a peripheral neural representation that is identical to that of the Huggins-pitch stimulus. The results show that processing in, or central to, the superior olivary complex can contribute to forward masking in human listeners