Heidi Christensen

• The University of Sheffield

Dementia is associated with various cognitive impairments and typically manifests only after significant progression , making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on...

This study investigates factors influencing Automatic Speech Recognition (ASR) systems' fairness and performance across genders, beyond the conventional examination of demographics. Using the LibriSpeech dataset and the Whisper small model, we analyze how performance varies across different gender representations in training data. Our findings sugg...

The early signs of cognitive decline are often noticeable in conversational speech, and identifying those signs is crucial in dealing with later and more serious stages of neurodegenerative diseases. Clinical detection is costly and time-consuming and although there has been recent progress in the automatic detection of speech-based cues, those sys...

Dementia poses a significant global challenge, with profound personal, societal, and economic impacts. Although it is incurable, early detection is crucial for ensuring appropriate care and support. Dementia can impair a person's speech and language abilities, and studies have demonstrated promising results in using spoken language for automatic de...

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on...

Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a ran...

Background This study aimed to explore the issues around developing a new automated cognitive assessment. Current cognitive screening or stratification tools were typically developed only using white people so having a normative data set from almost exclusively white populations. In this part of the project we are co‐developing an automated cogniti...

Background Stroke, post‐stroke dementia and post‐stroke cognitive impairment prevalence is rising significantly, placing an increasing burden on healthcare systems. Standardised pen and paper tools for cognitive assessment require clinical time. There is increasing research into the use of automated cognitive assessment. CognoSpeak (https://cognosp...

Objectives/Aims A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce misdiagnosis rates and waiting times. Most clinical decision tools fail to stratify between the three most common causes of TLOC (epilepsy, functional (dissociative) seizures, and syncope) or are hindered by the challenging differentiation between epile...

Background: Asking patients who have been referred to memory clinics open questions about recent events has been shown to have diagnostic relevance. Method: We use conversation analysis to look at responses to questions about recent events. The interviewees are healthy control (HC) participants, people with mild cognitive impairment (MCI), and peop...

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions....

The common causes of Transient Loss of Consciousness (TLOC) are syncope, epilepsy, and functional/dissociative seizures (FDS). Simple, questionnaire-based decision-making tools for non-specialists who may have to deal with TLOC (such as clinicians working in primary or emergency care) reliably differentiate between patients who have experienced syn...

Introduction Over 50% of stroke survivors have cognitive impairment. National guidelines promote early cognitive testing however, current pen-and-paper based tests are not always appropriate, typically take place in hospital and are time costly for busy clinicians. This project aimed to create an easy-to-use cognitive assessment tool specifically d...

Speech and language play an essential role in automatically assessing several psychotherapeutic qualities. These automation procedures require translating the manual rating qualities to speech and language features that accurately capture the assessed psychotherapeutic quality. Speech features can be determined by analysing recordings of psychother...

We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/family member. We describe an automated approach for determining the \textit{degree of warmth}, a key component of EE, from acoustic and text features acquired from a samp...

Background Automatic conversation analysis presents a useful aid for diagnosis and management of people with cognitive impairments, having already successfully differentiated between people with Alzheimer’s disease (AD) and healthy controls. Current clinical brief cognitive tests such as Addenbrooke’s Cognitive Examination are influenced by age, ed...

Investigating automatic methods for the early detection of dementia and related conditions that cause cognitive impairment is an area of growing interest. Video processing could play a role by providing a non-invasive and low-cost alternative to current expensive assessments. For this to be successful it is crucial that approaches are robust to in-...

A novel crowdsourcing project to gather children's storytelling based language samples using a mobile app was undertaken across the United Kingdom. Parents' scaffolding of children's narratives was observed in many of the samples. This study was designed to examine the relationship of scaffolding and young children’s narrative language ability in a...

Exploring acoustic and linguistic information embedded in spontaneous speech recordings has proven to be efficient for automatic Alzheimer’s dementia detection. Acoustic features can be extracted directly from the audio recordings, however, linguistic features, in fully automatic systems, need to be extracted from transcripts generated by an automa...

Background There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished from the other two causes relatively easily with a...

The early symptoms of neurodegenerative disorders, such as, Alzheimer’s dementia, frequently co-exist with symptoms of depression and anxiety. This phenomenon makes detecting dementia more difficult due to overlapping symptoms. Recent research has shown promising results on the automatic detection of depression and memory problems using features ex...

Objective There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished relatively easily with a small number of “yes”/”no”...

Aims The disease burden of cognitive impairment is significant and increasing. The aetiology of cognitive impairment can be structural, such as in mild cognitive impairment (MCI) due to early Alzheimer's disease (AD), or in functional cognitive disorder (FCD), where there is no structural pathology. Many people with FCD receive a delayed diagnosis...

Background The dramatic recent rise in referrals to specialist memory clinics has been associated with an increased proportion of patients referred with Functional Memory Disorder (FMD), i.e. non‐progressive cognitive complaints. These referrals have exerted time and financial pressures on secondary care services, impairing their ability to deliver...

Introduction Recent years have seen an almost sevenfold rise in referrals to specialist memory clinics. This has been associated with an increased proportion of patients referred with functional cognitive disorder (FCD), that is, non-progressive cognitive complaints. These patients are likely to benefit from a range of interventions (eg, psychother...

In the light of the current COVID-19 pandemic, the need for remote digital health assessment tools is greater than ever. This statement is especially pertinent for elderly and vulnerable populations. In this regard, the INTERSPEECH 2020 Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS) Challenge offers competitors the opportunity...

Speech and language based automatic dementia detection is of interest due to it being non-invasive, low-cost and potentially able to aid diagnosis accuracy. The collected data are mostly audio recordings of spoken language and these can be used directly for acoustic-based analysis. To extract linguistic-based information, an automatic speech recogn...

Speech-based automatic approaches for detecting neurodegenerative disorders (ND) and mild cognitive impairment (MCI) have received more attention recently due to being non-invasive and potentially more sensitive than current pen-and-paper tests. The performance of such systems is highly dependent on the choice of features in the classification pipe...

Objectives/Aims We used our automated cognitive assessment tool to explore whether responses to questions probing recent and remote memory could aid in distinguishing between patients with early neurodegenerative disorders and those with Functional Cognitive Disorders (FCD). Hypotheses: pwFCD would have no significant differences in pause to speech...

Automatic recognition of dysarthric speech is a very challenging research problem where performances still lag far behind those achieved for typical speech. The main reason is the lack of suitable training data to accommodate for the large mismatch seen between dysarthric and typical speech. Only recently has focus moved from single-word tasks to e...

The diagnosis of Mild Cognitive Impairment (MCI) characterises patients at risk of dementia and may provide an opportunity for disease-modifying interventions. Identifying persons with MCI (PwMCI) from adults of a similar age without cognitive complaints is a significant challenge. The main aims of this study were to determine whether generic speec...

There has been much recent interest in building continuous speech recognition systems for people with severe speech impairments, e.g., dysarthria. However, the datasets that are commonly used are typically designed for tasks other than ASR development, or they contain only isolated words. As such, they contain much overlap in the prompts read by th...

This paper presents an improved transfer learning framework applied to robust personalised speech recognition models for speakers with dysarthria. As the baseline of transfer learning, a state-of-theart CNN-TDNN-F ASR acoustic model trained solely on source domain data is adapted onto the target domain via neural network weight adaptation with the...

Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generat...

Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generat...

Cognitive decline is a sign of Alzheimer's disease (AD), and there is evidence that tracking a person's eye movement, using eye tracking devices, can be used for the automatic identification of early signs of cognitive decline. However, such devices are expensive and may not be easy-to-use for people with cognitive problems. In this paper, we prese...

Cognitive decline is a sign of Alzheimer's disease (AD), and there is evidence that tracking a person's eye movement, using eye tracking devices, can be used for the automatic identification of early signs of cognitive decline. However, such devices are expensive and may not be easy-to-use for people with cognitive problems. In this paper, we prese...

Picture description tasks are used for the detection of cognitive decline associated with Alzheimer’s disease (AD). Recent years have seen work on automatic AD detection in picture descriptions based on acoustic and word-based analysis of the speech. These methods have shown some success but lack an ability to capture any higher level effects of co...

This study investigates the deteriorating speech signal of people suffering from Alzheimer's disease (AD). The aim is to firstly predict a common clinical examination score used for dementia (MMSE) using acoustic information extracted from people describing a picture-a common dementia assessment task. Secondly, we aim to develop a diagnostic tool a...

Background: Individuals living with long-term physical health conditions frequently experience co-occurring mental health problems. This comorbidity has a significant impact on an individual’s levels of emotional distress, health outcomes, and associated health care utilization. As health care services struggle to meet demand and care increasingly...

Neurodegenerative diseases causing dementia are known to affect a person’s speech and language. Part of the expert assessment in memory clinics therefore routinely focuses on detecting such features. The current outpatient procedures examining patients’ verbal and interactional abilities mainly focus on verbal recall, word fluency, and comprehensio...

Improving the accuracy of personalised speech recognition for speakers with dysarthria is a challenging research field. In this paper, we explore an approach that non-linearly modifies speech tempo to reduce mismatch between typical and atypical speech. Speech tempo analysis at the phonetic level is accomplished using a forced-alignment process fro...

Generation and Rating of the EA Response Bank • Response bank generated by service users via online survey presenting 5 different SU scenarios; • Responses then screened for duplicates and usability by research team and rated by service users via online survey; • Responses then rated by SUs via online survey to generate ratings to be used in develo...

BACKGROUND Individuals living with long-term physical health conditions frequently experience co-occurring mental health problems. This comorbidity has a significant impact on individual’s levels of emotional distress, health outcomes and associated healthcare utilization. As healthcare services struggle to meet demand and care increasingly moves t...

Previous work on interactions in the memory clinic has shown that conversation analysis can be used to differentiate neurodegenerative dementia from functional memory disorder. Based on this work, a screening system was developed that uses a computerised ‘talking head’ (intelligent virtual agent) and a combination of automatic speech recognition an...

Neurogenerative disorders, like dementia, can affect a person's speech, language and as a consequence, conversational interaction capabilities. A recent study, aimed at improving dementia detection accuracy, investigated the use of conversation analysis (CA) of interviews between patients and neurologists as a means to differentiate between patient...

This study investigates the deteriorating speech signal of people suffering from Alzheimer's disease (AD). The aim is to firstly predict a common clinical examination score used for dementia (MMSE) using acoustic information extracted from people describing a picture -- a common dementia assessment task. Secondly, we aim to develop a diagnostic too...

Objects and Aims Referrals to secondary care memory clinic has more than tripled. This has led to increased demand on diagnostic services. Conversation Analysis (CA) can help in the screening for dementia. In CA important features of the conversation between doctor, patient are analysed. In this project we created an avatar to automatically initiat...

NeuroSpeech is a software for modeling pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. Although it was developed to model dysarthric speech signals from Parkinson's patients, its structure allows other computer scientists or developers to include other pathologies and/or me...

A better use of the increasing functional capabilities of home automation systems and Internet of Things (IoT) devices to support the needs of users with disability, is the subject of a research project currently conducted by Area Ausili (Assistive Technology Area), a department of Polo Tecnologico Regionale Corte Roncati of the Local Health Trust...

This paper presents work on developing an automatic dementia screening test based on patients’ ability to interact and communicate — a highly cognitively demanding process where early signs of dementia can often be detected. Such a test would help general practitioners, with no specialist knowledge, make better diagnostic decisions as current tests...

A new software for modeling pathological speech signals is presented in this paper. The software is called NeuroSpeech. This software enables the analysis of pathological speech signals considering different speech dimensions: phonation, articulation, prosody, and intelligibility. All the methods considered in the software have been validated in pr...

Change in voice quality (VQ) is one of the first precursors of Parkinson’s disease (PD). Specifically, impacted phonation and articulation causes the patient to have a breathy, husky-semiwhisper and hoarse voice. A goal of this paper is to characterize a VQ spectrum – the composition of non-modal phonations – of voice in PD. The paper relates non-m...

The main aim of the project described in this paper is to develop an experimental low cost system for environmental control through simplified user interfaces and voice control, to better respond to the needs of users with motor speech impairments (dysarthria). The project is actually being conducted by Area Ausili, a department of Polo Tecnologico...

Information from different bio–signals such as speech, hand- writing, and gait have been used to monitor the state of Parkinson’s disease (PD) patients, however, all the multi- modal bio–signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for lear...

Different modes of vibration of the vocal folds contribute significantly to the voice quality. The neutral mode phonation, often used in a modal voice, is one against which the other modes can be contrastively described, also called non-modal phonations. This paper investigates the impact of non-modal phonation on phonological posteriors, the proba...

The CloudCAST platform provides a series of speech recognition services that can be integrated into assistive technology applications. The platform and the services provided by the public API are described. Several exemplar applications have been developed to demonstrate the platform to potential developers and users.

This paper presents an overview of the studies that have been conducted with the purpose of understanding the use of brain signals as input to a speech recogniser. The studies have been categorised based on the type of the technology used with a summary of the methodologies used and achieved results. In addition, the paper gives an insight into som...

A recent study using Conversation Analysis (CA) has demonstrated that communication problems may be picked up during conversations between patients and neurologists, and that this can be used to differentiate between patients with (progressive neurodegenerative dementia) ND and those with (nonprogressive) functional memory disorders (FMD). This pap...

This paper introduces a new British English speech database, named the homeService corpus, which has been gathered as part of the homeService project. This project aims to help users with speech and motor disabilities to operate their home appliances using voice commands. The audio recorded during such interactions consists of realistic data of spe...

Background: The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentia...

Clinical applications of speech technology face two challenges. The first is data sparsity. There is little data available to underpin techniques which are based on machine learning and, because it is difficult to collect disordered speech corpora, the only way to address this problem is by pooling what is produced from systems which are already in...

Automated intelligibility assessments can support speech and language therapists in determining the type of dysarthria presented by their clients. Such assessments can also help predict how well a person with dysarthria might cope with a voice interface to assistive technology. Our approach to intelligibility assessment is based on iVectors, a set...

The automatic recognition of disordered speech is a domain that is characterised by limited amounts of training data for each speaker and large intra- and inter-speaker variations. This paper is concerned with how best to train an acoustic models in these circumstances; in particular, we look at how to select data for a background model from a pool...

The homeService research project is concerned with developing personalised speech-enabled interfaces, in an AAL setting, for users with severe physical impairments and associated disordered speech. By putting state-of-the-art speech recognition systems into people's homes, invaluable lessons can be learned from doing long-term trials `in-the-wild'....

Spoken control interfaces are very attractive to people with severe physical disabilities who often also have a type of speech disorder known as dysarthria. This condition is known to decrease the accuracy of automatic speech recognisers (ASRs) especially for users with moderate to severe dysathria. In this paper we investigate how applying probabi...

Speech technologies are more important every day to assist peo-ple with speech disorders. They can help to increase their qual-ity of life or help clinicians to make a diagnosis. In this paper a new methodology based on a total variability subspace mod-elled by factor analysis is proposed to assess the intelligibility of people with dysarthria. The...

Recently there has been increasing interest in ways of using out-of-domain (OOD) data to improve automatic speech recognition performance in domains where only limited data is available. This paper focuses on one such domain, namely that of disordered speech for which only very small databases exist, but where normal speech can be considered OOD. S...

This paper addresses the problem of speech recognition in reverberant multisource noise conditions using distant binaural microphones. Our scheme employs a two-stage fragment decoding approach inspired by Bregman's account of auditory scene analysis, in which innate primitive grouping ‘rules’ are balanced by the role of learnt schema-driven process...

Distant microphone speech recognition systems that operate with human-like robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech r...

One of the main clinical applications of speech technology is in voice-enabled assistive technology for people with disordered speech. Progress in this area is hampered by a sparseness in suitable data and recent research have focused on ways of incorporating knowledge about typical (i.e., un-impaired) speech through the use of e.g., deep belief ne...