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Found 13 papers, 2 papers with code
A Multimodal Approach to Device-Directed Speech Detection with Large Language Models
Interactions with virtual assistants typically start with a predefined trigger phrase followed by the user command.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing
It is well-known that the reparameterisation gradient estimator, which exhibits low variance in practice, is biased for non-differentiable models.
Multimodal Data and Resource Efficient Device-Directed Speech Detection with Large Foundation Models
We compare the proposed system to unimodal baselines and show that the multimodal approach achieves lower equal-error-rates (EERs), while using only a fraction of the training data.
A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem
Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom comes alone but rather co-occurs with others.
Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing
Thus we can prove stochastic gradient descent with the reparameterisation gradient estimator to be correct when applied to the smoothed problem.
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem
This work explores an approach based on a modified wav2vec 2. 0 system for end-to-end stuttering detection and classification as a multi-label problem.
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate
Recent findings show that pre-trained wav2vec 2. 0 models are reliable feature extractors for various speaker characteristics classification tasks.
Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?
The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech.
Nonwords Pronunciation Classification in Language Development Tests for Preschool Children
In this work, the task is to determine whether spoken nonwords have been uttered correctly.
The Influence of Dataset Partitioning on Dysfluency Detection Systems
This paper empirically investigates the influence of different data splits and splitting strategies on the performance of dysfluency detection systems.
Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0
This paper shows that fine-tuning wav2vec 2. 0 [1] for the classification of stuttering on a sizeable English corpus containing stuttered speech, in conjunction with multi-task learning, boosts the effectiveness of the general-purpose wav2vec 2. 0 features for detecting stuttering in speech; both within and across languages.
Detecting Vocal Fatigue with Neural Embeddings
Vocal fatigue refers to the feeling of tiredness and weakness of voice due to extended utilization.
Densities of Almost Surely Terminating Probabilistic Programs are Differentiable Almost Everywhere
We give SPCF a sampling-style operational semantics a la Borgstrom et al., and study the associated weight (commonly referred to as the density) function and value function on the set of possible execution traces.
