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eess.AS updates on arXiv.org

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling Predictive-Generative Drift Decomposition for Speech Enhancement and Separation Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation Alethia: A Foundational Encoder for Voice Deepfakes From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification Predicting Upcoming Stuttering Events from Three-Second Audio: Stratified Evaluation Reveals Severity-Selective Precursors, and the Model Deploys Fully On-Device The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation DiffAnon: Diffusion-based Prosody Control for Voice Anonymization Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost
Enhancement of Throat Microphone Recordings Using Gaussia...
Mehmet Ali Tugtekin Turan · 2018-04-13 · via eess.AS updates on arXiv.org

The throat microphone is a body-attached transducer that is worn against the neck. It captures the signals that are transmitted through the vocal folds, along with the buzz tone of the larynx. Due to its skin contact, it is more robust to the environmental noise compared to the acoustic microphone that picks up the vibrations through air pressure, and hence the all interventions. The throat speech is partly intelligible, but gives unnatural and croaky sound. This thesis tries to recover missing frequency bands of the throat speech and investigates envelope and excitation mapping problem with joint analysis of throat- and acoustic-microphone recordings. A new phone-dependent GMM-based spectral envelope mapping scheme, which performs the minimum mean square error (MMSE) estimation of the acoustic-microphone spectral envelope, has been proposed. In the source-filter decomposition framework, we observed that the spectral envelope difference of the excitation signals of throat- and acoustic-microphone recordings is an important source of the degradation in the throat-microphone voice quality. Thus, we also model spectral envelope difference of the excitation signals as a spectral tilt vector, and propose a new phone-dependent GMM-based spectral tilt mapping scheme to enhance throat excitation signal. Experimental evaluations are performed to compare the proposed mapping scheme using both objective and subjective evaluations. Objective evaluations are performed with the log-spectral distortion (LSD) and the wide-band perceptual evaluation of speech quality (PESQ) metrics. Subjective evaluations are performed with A/B pair comparison listening test. Both objective and subjective evaluations yield that the proposed phone-dependent mapping consistently improves performances over the state-of-the-art GMM estimators.