This paper addresses the common challenge of system performance degradation due to speech inconsistency and mismatched acoustic conditions across various domains in speaker verification tasks. We propose a Noise-Aware Quality Network designed to estimate a score based on speech quality and the presence of speech obscured by noise in real-world environments. The score, derived from the normalization of estimated speech quality evaluations, is incorporated into a proposed Noise-Aware Quality loss function, aiming to prioritize speech quality by weighting the embedding distances based on the quality score. Our methodology significantly improves speaker verification performance, particularly in noisy environments. Furthermore, our work highlights the importance of speech quality and the potential benefits of incorporating speech quality weight into the loss function for speaker verification tasks.

In this study, we explore the influence of Thai Linguistic Units (TH-LUs) and speech trimming on the state-of-the-art Thai Text-to-Speech (TTS) systems.We propose an end-to-end Thai TTS framework that emphasizes phonemes, syllables, and words, essential for accurate text pronunciation.To thoroughly investigate these aspects, we designed two main experiments: the TH-LU factor experiment and the TH-LU with speech trimming factor experiment.Our assessment targeted speaker tone and pronunciation accuracy.VITS model demonstrated a standout performer in tonal accuracy, which is evaluated by the Speaker Encoder Cosine Similarity (SECS) method, across different TH-LUs in both trim and non-trim speech training data.For pronunciation accuracy, we integrated a Thai speech-to-text model to evaluate.Our results indicate that VITS with the word linguistic unit outperforms all baselines in overall performance, excelling in both speaker tone and pronunciation accuracy.This research significantly advances the field of TTS, particularly for the Thai language, by highlighting the importance of diverse TH-LU and speech trimming in TTS model development and underlining the need for evaluation methods that account for both tonal accuracy and pronunciation quality.