Advancing Electrolaryngeal Speech Enhancement Through Speech-Text Representation Learning
Abstract
Objective: laryngectomees depend on an electromechanical device to generate electrolaryngeal (EL) speech. Compared with normal speech, EL speech suffers from severe distortion, limited phonetic variation, unnatural prosody, and temporal shifts, degrading naturalness and intelligibility. Although sequence-to-sequence (seq2seq) voice conversion (VC) based EL-speech-to-normal-speech conversion (EL2SP) is promising, substantial mismatches between EL and normal speech inevitably cause cumulative mapping errors that limit performance. To address this, we describe a novel representation learning framework integrating speech and text representations to improve mapping and reconstruction quality within a seq2seq VC model. Methods: our methodology comprises two main stages: 1) representation integration and learning, and 2) reconstruction training. A network capable of incorporating auxiliary text information is first constructed with pretrained modules to learn speech--text-based integrated representations. Then, an autoencoder-style reconstruction strategy finalizes EL2SP model to inherit these representations without increasing model complexity. We introduce three fusion strategies including middle-, input-, and hybrid-level fusion strategies that progressively enhance learning. Moreover, besides standard seq2seq VC objectives, an additional reconstruction loss on the integrated representation is introduced to refine representation transfer. Results: experiments under different EL2SP datasets consistently demonstrate that our methods, combined with data augmentations, outperform baselines relying solely on speech representations. Furthermore, progressive improvements with system design depth validate the effectiveness of our methods. Significance: the proposed methods provide an extensible and practical methodology for EL speech enhancement and assistive communication technologies.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the development of a novel representation learning framework that integrates speech and text for enhancing electrolaryngeal speech. This work significantly advances the field of voice conversion by addressing the inherent challenges faced by laryngectomees, providing a practical and extensible solution that can greatly improve assistive communication technologies.
Methodology
The paper proposes a novel seq2seq-based framework for electrolaryngeal (EL) speech enhancement by integrating speech and text representations. The methodology is well-structured, comprising a two-stage approach that includes representation integration and reconstruction training. The introduction of three distinct fusion strategies (middle-, input-, and hybrid-level) is innovative, allowing for enhanced learning of speech-text representations. The use of auxiliary text information to improve mapping quality is a significant advancement over traditional methods that rely solely on speech representations. The incorporation of an autoencoder-style reconstruction strategy is also a notable contribution, as it maintains model simplicity while improving performance.
Results & evaluation
The experimental evaluation is robust, utilizing multiple small-scale EL2SP datasets that reflect real-world challenges in data collection for laryngectomees. The results demonstrate consistent improvements over baseline models, indicating the effectiveness of the proposed methods. The use of both objective metrics (MCD, CER, F0 RMSE) and subjective evaluations (MOS for naturalness and intelligibility) provides a comprehensive assessment of the system's performance. The statistical significance of the results strengthens the claims made by the authors regarding the superiority of their approach.
Reproducibility
The paper provides sufficient details regarding the implementation of the proposed systems, including the architecture, training procedures, and evaluation metrics. The use of established frameworks like ESPnet and the clear description of datasets and experimental protocols enhance reproducibility. However, the absence of publicly available code or detailed hyperparameter settings may hinder full reproducibility for some researchers.
Limitations
One limitation of the study is the reliance on small-scale datasets, which may not fully capture the variability present in natural speech. Additionally, while the proposed methods show improvements, the performance may still not reach the level of natural human speech, indicating room for further enhancement. The complexity of the model increases with the incorporation of multiple fusion strategies, which may pose challenges in practical implementations.
Broader impact
The proposed methods have significant implications for assistive communication technologies, particularly for individuals who rely on electrolaryngeal devices. By improving the naturalness and intelligibility of EL speech, the research can enhance communication quality for laryngectomees, thus potentially improving their quality of life. The integration of speech and text representations may also inspire further research in multimodal speech processing and voice conversion applications. The main contribution of this paper is the development of a novel representation learning framework that integrates speech and text for enhancing electrolaryngeal speech. This work significantly advances the field of voice conversion by addressing the inherent challenges faced by laryngectomees, providing a practical and extensible solution that can greatly improve assistive communication technologies.
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