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arxiv_cl 95% Match Research Paper Speech Researchers,ML Engineers,Audio Developers 1 week ago

Efficient Speech Language Modeling via Energy Distance in Continuous Latent Space

speech-audio › text-to-speech
📄 Abstract

Abstract: We introduce SLED, an alternative approach to speech language modeling by encoding speech waveforms into sequences of continuous latent representations and modeling them autoregressively using an energy distance objective. The energy distance offers an analytical measure of the distributional gap by contrasting simulated and target samples, enabling efficient training to capture the underlying continuous autoregressive distribution. By bypassing reliance on residual vector quantization, SLED avoids discretization errors and eliminates the need for the complicated hierarchical architectures common in existing speech language models. It simplifies the overall modeling pipeline while preserving the richness of speech information and maintaining inference efficiency. Empirical results demonstrate that SLED achieves strong performance in both zero-shot and streaming speech synthesis, showing its potential for broader applications in general-purpose speech language models.
Authors (6)
Zhengrui Ma
Yang Feng
Chenze Shao
Fandong Meng
Jie Zhou
Min Zhang
Submitted
May 19, 2025
arXiv Category
cs.CL
arXiv PDF

Key Contributions

Introduces SLED, an alternative approach to speech language modeling using continuous latent representations and an energy distance objective. It bypasses discretization errors and complex hierarchical architectures common in existing models, simplifying the pipeline while preserving speech richness.

Business Value

Enables more efficient and higher-quality text-to-speech systems, improving applications like virtual assistants, audiobooks, and accessibility tools.

Paper Metadata

Innovation Type

Methodology/Architecture

Deployment Feasibility

Moderate, requires specialized training infrastructure but simplifies the inference pipeline.

Limitations Addressed

Discretization errors and complex hierarchical architectures in existing speech language models, simplifying the modeling pipeline.

Performance Gains

Achieves strong performance in both zero-shot and streaming speech synthesis.

Technical Tags

speech language modelingenergy distancecontinuous latent spacewaveform encodingautoregressive modelingdiscretization errorshierarchical architectureszero-shot synthesisstreaming synthesisSLED

Research Topics

Speech SynthesisSpeech ProcessingGenerative ModelsDeep LearningAutoregressive Models

Methods & Architectures

Speech waveform encoding into continuous latent representationsAutoregressive modelingEnergy distance objectiveBypassing residual vector quantization Autoregressive Models

Applications & Tasks

Speech Synthesis Voice Generation Audio Processing Discretization errors in speech modelingComplicated hierarchical architecturesReliance on residual vector quantizationSimplifying speech language modeling pipeline Speech language modelingZero-shot speech synthesisStreaming speech synthesisGenerating high-quality speech

Related Fields

Machine LearningDeep LearningSignal ProcessingAudio Engineering

Keywords

speech synthesislanguage modelingenergy distancecontinuous latent spaceautoregressivewaveformzero-shotstreamingtext-to-speechSLED

Academic Context

#Speech Synthesis#Speech Processing#Generative Models#Deep Learning#Autoregressive Models

Commercial Potential

Potential Products
Advanced TTS enginesVoice cloning toolsReal-time speech generation systems
Target Industries
MediaGamingCustomer ServiceAccessibilityTechnology
Use Case Examples
Generating natural-sounding voices for virtual assistants.Creating realistic voiceovers for videos and podcasts.Enabling real-time speech generation for interactive applications.
Competitive Edge
Offers a simplified and more effective approach to speech language modeling compared to existing methods that rely on discretization and complex architectures.
Market Opportunity
Growing market for high-quality synthetic speech.
Revenue Models
Licensing of TTS technologyAPI services.

Resource Requirements

Compute Needs
High (for training)
Data Requirements
Large speech datasets
Deployment Constraints
Requires efficient implementation of the continuous latent space modeling.
Scalability
Scalability depends on the autoregressive model's efficiency.

Production Readiness

Maturity Level

Research

Time to Market

2-3 years for commercial integration.

Patent Potential

Moderate (for the energy distance application in speech modeling)

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