The Normal-To-Lombard (N2L) speech conversion can effectively improve speech intelligibility in noisy communication scenarios and serve as a data augmentation tool for various speech-related algorithms. However, existing N2L methods did not aim to disentangle the Lombard effect from other speech attributes, leading to incomplete conversions. In this paper, we propose a Parameter-Guided Disentanglement approach for N2L speech conversion (PGD-N2L) which decomposes speech into linguistic content, speaker identity, and Lombard effect. To extract disentangled linguistic content, we propose a DeLomb-Based content encoder. To extract disentangled speaker identity and Lombard effect, we propose a style encoder that combines a fine-tuned speaker encoder and a learnable Lombard encoder to form a personalized style embedding. Furthermore, an En-Lomb-Based injection module is designed to accurately integrate the target Lombard effect and speaker identity into the linguistic content based on personalized style embedding, ensuring complete Lombard conversion. Experimental results demonstrate that our proposed method outperforms existing N2L models in speech intelligibility, acoustic similarity, and speech quality. Ablation studies confirm that the fine-tuned speaker encoder and the De-Lomb block effectively improve speech intelligibility and acoustic similarity, while the En-Lomb block enables the converted speech to more closely match the target Lombard speech.

This study investigates the Lombard effect, where individuals adapt their speech in noisy environments. We introduce an enhanced Mandarin Lombard grid (EMALG) corpus with meaningful sentences, enhancing the Mandarin Lombard grid (MALG) corpus. EMALG features 34 speakers and improves recording setups, addressing challenges faced by MALG with nonsense sentences. Our findings reveal that in Mandarin, meaningful sentences are more effective in enhancing the Lombard effect. Additionally, we uncover that female exhibit a more pronounced Lombard effect than male when uttering meaningful sentences. Moreover, our results reaffirm the consistency in the Lombard effect comparison between English and Mandarin found in previous research.

This paper presents a Paired Mandarin Matrix Sentence Dataset (PMMSD), which will be available after publication. PMMSD is the first Mandarin matrix sentence intelligibility dataset containing both plain and Lombard speech for scientific research. The results verify that different Lombard styles would affect word intelligibility to different degrees and the Lombard effect helps maintain homogeneous intelligibility against contextual interference. All of the discoveries indicate that the Lombard effect should be considered when building intelligibility datasets with noise in the future.