#acoustic-modeling News & Analysis

6 articles tagged with #acoustic-modeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AINeutralarXiv – CS AI · Jun 236/10

🧠

EmoInstruct-TTS: Dual-Path Instruction-Guided Emotional Speech Synthesis

EmoInstruct-TTS introduces a dual-path framework for emotional speech synthesis that enables fine-grained emotional control through natural language instructions. The system uses Emotion2embed, covering 48 emotional states, and an Instruction-Conditioned Emotion Flow Model to convert free-form text instructions into acoustically grounded emotion representations integrated with LLM-based synthesis pipelines.

AINeutralarXiv – CS AI · Jun 196/10

🧠

Cross-Dataset, Age, and Gender Generalization: A Comprehensive Analysis of Fine-Tuning Strategies for Low-Resource Children's ASR

Researchers have developed improved acoustic modeling techniques for recognizing dysarthric speech in children, achieving 4.65% relative improvement in word recognition and 4.63% in sentence recognition using Factorized Time Delay Neural Networks. The study demonstrates that strategic selection of acoustic features, particularly pitch characteristics, significantly enhances performance on low-resource speech recognition tasks.

AINeutralarXiv – CS AI · Jun 96/10

🧠

GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model

GenTSE introduces a two-stage generative language model for target speaker extraction that separates semantic and acoustic token generation, demonstrating improved speech quality and speaker consistency over previous LM-based approaches. The system employs novel training strategies including Frozen-LM Conditioning and Direct Preference Optimization to reduce exposure bias and align outputs with human perceptual preferences.

AINeutralarXiv – CS AI · Jun 85/10

🧠

Phonetic Error Analysis of Raw Waveform Acoustic Models

Researchers achieved state-of-the-art performance on raw waveform acoustic models for phone recognition using CNN-LSTM architectures, with error rates of 13.9%/15.3% on TIMIT benchmarks. Analysis reveals that different phonetic classes benefit differently from model components, and transfer learning from WSJ data improves consonant recognition significantly more than vowels.

AINeutralarXiv – CS AI · Jun 46/10

🧠

Neural Radiated-Noise Fields for Unmanned Underwater Vehicle Noise Spectrum Prediction in Three-Dimensional Scenes

Researchers have developed a neural radiated-noise field (NRNF) model that predicts underwater vehicle acoustic signatures across three-dimensional spaces using machine learning rather than traditional physics-based simulation. The model achieves 3.5 dB average prediction error in the 50-5000 Hz band and demonstrates improved spatial generalization through a learnable scene feature grid.

AINeutralarXiv – CS AI · May 115/10

🧠

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation

Researchers decomposed room impulse responses to understand which acoustic components enable single-channel speaker distance estimation, finding that without time calibration, models rely on early reflections and achieve 1.29m error, while time-calibrated models achieve 0.14m accuracy using propagation delay alone.