Innovations in Speech and Language Processing

The field of speech and language processing is rapidly advancing, with a focus on developing more accurate and robust models for various applications. Recent research has explored the use of unsupervised learning techniques, such as PCA and clustering, to identify natural language development trajectories in children with and without Specific Language Impairment (SLI). Additionally, there is a growing interest in using speech embeddings to analyze linguistic relationships across languages and dialects. Another area of focus is the development of multilingual speech emotion recognition systems, which can be achieved through language-aware multi-teacher knowledge distillation methods. Furthermore, research has shown that pre-trained language models can learn remarkably accurate representations of numbers, which can be decoded with near-perfect accuracy using novel probing techniques. Other notable advancements include the introduction of new datasets, such as FROST-EMA, which enables research into language variability from phonetic and technological points of view, and the development of techniques for isolating lexically-independent phonetic dependencies in generative CNNs. The incorporation of linguistic constraints from external knowledge sources, such as pre-trained speech-language models and pre-trained language models, has also been explored for audio-visual target speech extraction. Noteworthy papers include:

  • The study on multidimensional analysis of SLI using unsupervised learning, which challenges categorical diagnostic frameworks and highlights the potential of unsupervised learning techniques for refining diagnostic criteria and intervention strategies.
  • The research on pre-trained language models learning remarkably accurate representations of numbers, which proves that these models represent numbers with remarkable precision and can mitigate arithmetic errors.
  • The introduction of the FROST-EMA dataset, which enables research into language variability from phonetic and technological points of view.
  • The development of a novel technique for probing a model's lexically-independent generalizations, which shows that convolutional layers can dynamically generalize phonetic dependencies beyond lexically-constrained configurations learned by the FC.

Sources

Multidimensional Analysis of Specific Language Impairment Using Unsupervised Learning Through PCA and Clustering

Neighbors and relatives: How do speech embeddings reflect linguistic connections across the world?

Multi-Teacher Language-Aware Knowledge Distillation for Multilingual Speech Emotion Recognition

Pre-trained Language Models Learn Remarkably Accurate Representations of Numbers

FROST-EMA: Finnish and Russian Oral Speech Dataset of Electromagnetic Articulography Measurements with L1, L2 and Imitated L2 Accents

Employing self-supervised learning models for cross-linguistic child speech maturity classification

A Technique for Isolating Lexically-Independent Phonetic Dependencies in Generative CNNs

Incorporating Linguistic Constraints from External Knowledge Source for Audio-Visual Target Speech Extraction

Unsupervised Protoform Reconstruction through Parsimonious Rule-guided Heuristics and Evolutionary Search

Spelling-out is not Straightforward: LLMs' Capability of Tokenization from Token to Characters

Analyzing the relationships between pretraining language, phonetic, tonal, and speaker information in self-supervised speech models

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