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Found 22 papers, 2 papers with code
A multi-modal approach for identifying schizophrenia using cross-modal attention
This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms.
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables
The performance of deep learning models depends significantly on their capacity to encode input features efficiently and decode them into meaningful outputs.
Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults
Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speaker-independent Speech Inversion for Estimation of Nasalance
Nasalance is an objective measure derived from the oral and nasal acoustic signals that correlate with nasality.
Acoustic-to-Articulatory Speech Inversion Features for Mispronunciation Detection of /r/ in Child Speech Sound Disorders
Acoustic-to-articulatory speech inversion could enhance automated clinical mispronunciation detection to provide detailed articulatory feedback unattainable by formant-based mispronunciation detection algorithms; however, it is unclear the extent to which a speech inversion system trained on adult speech performs in the context of (1) child and (2) clinical speech.
Learning to Compute the Articulatory Representations of Speech with the MIRRORNET
Most organisms including humans function by coordinating and integrating sensory signals with motor actions to survive and accomplish desired tasks.
The Secret Source : Incorporating Source Features to Improve Acoustic-to-Articulatory Speech Inversion
The proposed SI system with the HPRC dataset gains an improvement of close to 28% when the source features are used as additional targets.
Masked Autoencoders Are Articulatory Learners
Articulatory recordings track the positions and motion of different articulators along the vocal tract and are widely used to study speech production and to develop speech technologies such as articulatory based speech synthesizers and speech inversion systems.
An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models
Knowing that harmonicity is a critical cue for these networks to group sources, in this work, we perform a thorough investigation on ConvTasnet and DPT-Net to analyze how they perform a harmonic analysis of the input mixture.
Acoustic-to-articulatory Speech Inversion with Multi-task Learning
Multi-task learning (MTL) frameworks have proven to be effective in diverse speech related tasks like automatic speech recognition (ASR) and speech emotion recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs
In this work, we compare and contrast different ways of doing data augmentation and show how this technique improves the performance of articulatory speech inversion not only on noisy speech, but also on clean speech data.
Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals
We used U. of Wisconsin X-ray Microbeam (XRMB) database of clean speech signals to train a feed-forward deep neural network (DNN) to estimate articulatory trajectories of six tract variables.
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems
We evaluate their performance with mixtures of natural speech versus slightly manipulated inharmonic speech, where harmonics are slightly frequency jittered.
Ranked #1 on
Adversarial Attack
on WSJ0-2mix
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings
The multimodal system is developed by combining embeddings from the session-level audio model and the HAN text model
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e. g. hallucinations and delusions), using two distinct channel-delay correlation methods.
Speech based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model
The ACFs derived from the vocal tract variables (TVs) are used to train a dilated Convolutional Neural Network based depression classification model to obtain segment-level predictions.
Generalized Dilated CNN Models for Depression Detection Using Inverted Vocal Tract Variables
We show that ACFs derived from Vocal Tract Variables (TVs) show promise as a robust set of features for depression detection.
Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop
Fourth, more powerful adaptation methods are needed, to enable robots to communicate in new environments, for new tasks, and with diverse user populations, without extensive re-engineering or the collection of massive training data.
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks
In this work, we experiment with variants of GAN architectures to generate feature vectors corresponding to an emotion in two ways: (i) A generator is trained with samples from a mixture prior.
On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
GANs consist of a discriminator and a generator working in tandem playing a min-max game to learn a target underlying data distribution; when fed with data-points sampled from a simpler distribution (like uniform or Gaussian distribution).
Semi-supervised and Transfer learning approaches for low resource sentiment classification
Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event.
Adversarial Auto-encoders for Speech Based Emotion Recognition
Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition.
