Optimized Speech Representation and Processing Innovations

Current Trends in Speech Representation and Processing

Recent advancements in speech technology have seen a significant shift towards more nuanced and specialized models, particularly in the areas of speech representation learning, emotion recognition, and voice cloning. The field is increasingly focusing on optimizing models for specific tasks, such as kinship verification and target-speaker speech processing, by developing frameworks that can handle multiple aspects of speech information independently. This approach allows for more efficient and effective learning, as evidenced by the superior performance of models like JOOCI in downstream tasks.

Another notable trend is the integration of self-supervised learning (SSL) features to enhance performance in tasks such as speech emotion recognition (SER). Innovations like Segmental Average Pooling (SAP) are addressing the limitations of traditional Global Average Pooling (GAP) by focusing on informative speech segments, leading to improved accuracy in SER.

Voice cloning has also seen remarkable progress, with models like SF-Speech demonstrating the feasibility of achieving high-quality zero-shot voice cloning on small-scale datasets. These models leverage advanced techniques such as ordinary differential equations and contextual learning to achieve significant improvements in speech intelligibility and timbre similarity.

Noteworthy papers include:

  • JOOCI: Demonstrates significant performance gains in speech downstream tasks by optimizing content and other information independently.
  • SF-Speech: Achieves impressive zero-shot voice cloning results on small datasets, outperforming existing models in speech intelligibility and timbre similarity.
  • SAP for SER: Introduces a novel approach to pooling SSL features, significantly enhancing SER performance by focusing on informative speech segments.

These developments highlight the ongoing innovation and refinement in speech technology, pushing the boundaries of what is possible in speech representation and processing.

Sources

JOOCI: a Framework for Learning Comprehensive Speech Representations

Audio-based Kinship Verification Using Age Domain Conversion

Investigation of Speaker Representation for Target-Speaker Speech Processing

SF-Speech: Straightened Flow for Zero-Shot Voice Clone on Small-Scale Dataset

Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features

HeightCeleb -- an enrichment of VoxCeleb dataset with speaker height information

Empowering Dysarthric Speech: Leveraging Advanced LLMs for Accurate Speech Correction and Multimodal Emotion Analysis

What Do Speech Foundation Models Not Learn About Speech?

EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning

Roadmap towards Superhuman Speech Understanding using Large Language Models

End-to-End Integration of Speech Emotion Recognition with Voice Activity Detection using Self-Supervised Learning Features

Enhancing 1-Second 3D SELD Performance with Filter Bank Analysis and SCConv Integration in CST-Former

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