Improving Dysarthric Speech Recognition by Enriching Training Datasets

Dysarthria is a motor speech disorder that results from disruptions in the neuro-motor interface and is characterised by poor articulation of phonemes and hyper-nasality and is characteristically different from normal speech. Many modern automatic speech recognition systems focus on a narrow range of speech diversity therefore as a consequence of this they exclude a groups of speakers who deviate in aspects of gender, race, age and speech impairment when building training datasets. This study attempts to develop an automatic speech recognition system that deals with dysarthric speech with limited dysarthric speech data. Speech utterances collected from the TORGO database are used to conduct experiments on a wav2vec2.0 model only trained on the Librispeech 960h dataset to obtain a baseline performance of the word error rate (WER) when recognising dysarthric speech. A version of the Librispeech model fine-tuned on multi-language datasets was tested to see if it would improve accuracy and achieved a top reduction of 24.15% in the WER for one of the male dysarthric speakers in the dataset. Transfer learning with speech recognition models and preprocessing dysarthric speech to improve its intelligibility by using general adversarial networks were limited in their potential due to a lack of dysarthric speech dataset of adequate size to use these technologies. The main conclusion drawn from this study is that a large diverse dysarthric speech dataset comparable to the size of datasets used to train machine learning ASR systems like Librispeech,with different types of speech, scripted and unscripted, is required to improve performance.

Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches

Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people. Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy. The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech. In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

Learning Outcomes of Classroom Research

Personal pronouns are a linguistic device that is used to engage students at various educational levels. Personal pronouns are multifunctional, and their functions range from inclusion to exclusion, and include establishing of rapport with students. In this chapter, we compare the use of personal pronouns at university and secondary school levels. Our previous study (Yeo & Ting, 2014) showed the frequent use of you in lecture introductions (2,170 instances in the 37,373-word corpus) to acknowledge the presence of students. The arts lecturers were more inclusive than the science lecturers, reflected in the less frequent use of exclusive-we and we for one, as well as the frequent use of you-generalised. We have also compiled and analysed a 43,511-word corpus from 15 English lessons in three Malaysian secondary schools. This corpus yielded 2,019 instances of personal pronoun use. The results showed that you was the most frequently used personal pronoun, followed by we and I. You-audience was used more than you-generalised, and the main function was to give instructions to students. The teachers appeared to be more directive than the lecturers in the previous study, who sometimes used the inclusive-we for you and I and we for I to lessen the social distance with students, indicating that the discourse functions of personal pronouns vary with the educational context. The findings suggest that educators can be alerted to the versatility of personal pronouns, for example, for engaging students in the lesson and for asserting authority in the subject matter. Keywords: student engagement; personal pronouns; lecture; classroom; teache