Accurate pronunciation of speech sounds is essential in communication. As children learn their native language, they refine the movements necessary for intelligible speech. While there is variability in the order of acquisition of speech sounds, ther...
Speech Emotion Classification (SEC) relies heavily on the quality of feature extraction and selection from the speech signal. Improvement on this to enhance the classification of emotion had attracted significant attention from researchers. Many prim...
This work presents a method for classifying EEG (Electroencephalogram) signals generated when a person concentrates on specific words, defined as “Imagined Speech”. Imagined speech is essential to enhance problem-solving, memory, and lang...
Background/Objectives: Automatic pain detection from speech signals holds strong promise for non-invasive and real-time assessment in clinical and caregiving settings, particularly for populations with limited capacity for self-report. Methods: In th...
This study aims to develop effective screening tools for cognitive impairment by integrating optimised speech classification features with various machine learning models. A total of 65 patients diagnosed with early-stage Mild Cognitive Impairment (M...
Many speech emotion recognition systems have been designed using different features and classification methods. Still, there is a lack of knowledge and reasoning regarding the underlying speech characteristics and processing, i.e., how basic characte...
Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have signif...
Background: Alzheimer’s disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass scree...
Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred to...
Recently, automatic speech recognition (ASR) and visual speech recognition (VSR) have been widely researched owing to the development in deep learning. Most VSR research works focus only on frontal face images. However, assuming real scenes, it is ob...
Feature selection and feature extraction have always been of utmost importance owing to their capability to remove redundant and irrelevant features, reduce the vector space size, control the computational time, and improve performance for more accur...
Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also. Initially, the primary motivation behind spectrogram-based representations was their...
Parkinson’s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis...
Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be...
The competition of speech recognition technology related to smartphones is now getting into full swing with the widespread internet of thing (IoT) devices. For robust speech recognition, it is necessary to detect speech signals in various acoustic en...
Impulsivity is a common trait and is understood as a symptom of various disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). We previously proposed ImpulsivityBank protocol, which is a standardized discourse protocol for retrieving spee...
Background/Objectives: Speech produced by individuals with hearing loss differs notably from that of normal-hearing (NH) individuals. Although cochlear implants (CIs) provide sufficient auditory input to support speech acquisition and control, there...
The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI). In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTI...
Speech emotion recognition (SER) plays a significant role in human–machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate...
Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by s...
Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese...
Speech signals are being used as a primary input source in human–computer interaction (HCI) to develop several applications, such as automatic speech recognition (ASR), speech emotion recognition (SER), gender, and age recognition. Classifying speake...
Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker’s features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves thre...
The most used and well-known acoustic features of a speech signal, the Mel frequency cepstral coefficients (MFCC), cannot characterize emotions in speech sufficiently when a classification is performed to classify both discrete emotions (i.e., anger,...
Speech-based depression detection is promising for objective mental health assessment. However, conventional methods relying on short-frame acoustic features often fail to capture long-term temporal and behavioral characteristics of speech essential...
Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson’s disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0...
15 January 2023
Audio features are physical features that reflect single or complex coordinated movements in the vocal organs. Hence, in speech-based automatic depression classification, it is critical to consider the relationship among audio features. Here, we prop...
Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classi...
The conventional speech recognition systems can handle the input speech of a specific single language. To realize multi-lingual speech recognition, a language should be firstly identified from input speech. This study proposes an efficient Language I...
As speech is the most natural way for humans to express emotions, studies on Speech Emotion Recognition (SER) have been conducted in various ways However, there are some areas for improvement in previous SER studies: (1) while some studies have perfo...
The goal of the human interface is to recognize the user’s emotional state precisely. In the speech emotion recognition study, the most important issue is the effective parallel use of the extraction of proper speech features and an appropriate class...
The characterising sound required for the Acoustic Scene Classification (ASC) system is contained in the ambient signal. However, in practice, this is often distorted by e.g., foreground speech of the speakers in the surroundings. Previously, based o...
Speech signals contain abundant information on personal emotions, which plays an important part in the representation of human potential characteristics and expressions. However, the deficiency of emotion speech data affects the development of speech...
Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of spe...
Speech segment detection based on gated recurrent unit (GRU) recurrent neural networks for the Kurdish language was investigated in the present study. The novelties of the current research are the utilization of a GRU in Kurdish speech segment detect...
Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (human-computer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement....
In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several...
This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech...
Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classificat...
This article describes a system for analyzing acoustic data to assist in the diagnosis and classification of children’s speech sound disorders (SSDs) using a computer. The analysis concentrated on identifying and categorizing four distinct type...
The problem of accent recognition has received a lot of attention with the development of Automatic Speech Recognition (ASR) systems. The crux of the problem is that conventional acoustic language models adapted to fit standard language corpora are u...
Emotion recognition in speech has gained increasing relevance in recent years, enabling more personalized interactions between users and automated systems. This paper presents the development of a dataset of features obtained from RAVDESS (Ryerson Au...
Automated techniques to detect Alzheimer’s Dementia through the use of audio recordings of spontaneous speech are now available with varying degrees of reliability. Here, we present a systematic comparison across different modalities, granulari...
Speech forgery and tampering, increasingly facilitated by advanced audio editing software, pose significant threats to the integrity and privacy of digital speech avatars. Speech resampling is a post-processing operation of various speech-tampering m...
Multimodality has been widely used for sentiment analysis tasks, especially for speech sentiment analysis. Compared with the emotion expression of most text languages, speech is more intuitive for human emotion, as speech contains more and richer emo...
Obstructive sleep apnea (OSA) is a prevalent disorder for which accessible non-invasive screening approaches remain of interest. This study evaluated photographic craniofacial descriptors and speech-derived acoustic features for Apnea–Hypopnea...
Voice data contain a wealth of temporal and spectral information and can be a valuable resource for disease classification. However, traditional methods are often not effective in capturing the key features required for the classification of multiple...
The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency mon...
Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsi...
Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a...
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