MELD: Mel-Spectrogram-Based Speech Language Modeling with Discrete Latent Variables
Abstract
Recent speech language models rely on encoders that are optimized separately from autoregressive models. Since these encoders are unaware of the downstream objectives, the extracted representations may not be optimal for downstream tasks. To address this limitation, we introduce a discrete latent variable model on mel spectrograms that jointly optimizes the encoder and the speech language model. Joint optimization not only brings improvements over codec-based and other mel-spectrogram-based baselines on zero-shot Text-to-Speech (TTS) and Speech-to-Text (STT) tasks, but also effectively alleviates common issues in autoregressive mel-spectrogram modeling, such as prolonged silence generation and word omissions.
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 work is the introduction of MELD, a joint optimization framework for speech language modeling that effectively integrates discrete latent variables to enhance TTS and STT performance. This approach represents a significant advancement in the field, addressing key limitations of existing methods and paving the way for future research in multimodal speech processing.
Methodology
The paper presents a novel approach to speech language modeling by introducing MELD, which integrates discrete latent variables into the autoregressive modeling of mel-spectrograms. This joint optimization of the encoder and autoregressive model addresses limitations of previous two-stage methods, particularly in preserving task-relevant information. The methodology is well-structured, leveraging variational inference to optimize a lower bound on the log likelihood, and effectively incorporates both TTS and STT tasks within a single framework. The use of discrete latent variables to suppress silence generation is a significant innovation, enhancing the model's performance over existing methods.
Results & evaluation
The experiments are comprehensive, utilizing the 960-hour subset of the LibriSpeech dataset for training and evaluation. The authors compare MELD against several baselines, including codec-based models and other mel-spectrogram-based approaches, demonstrating clear improvements in both TTS and STT tasks. The evaluation metrics include both subjective (MOS, speaker similarity) and objective (WER) assessments, providing a well-rounded view of the model's performance. The results indicate that MELD outperforms its competitors, particularly in reducing silence and improving word error rates.
Reproducibility
The paper provides detailed implementation specifics, including model architecture, training configurations, and evaluation protocols. However, the authors acknowledge challenges in reproducing results from related work (e.g., MELLE), which may affect the perceived reliability of their comparisons. The use of specific datasets and training strategies is well-documented, but the lack of a public code repository or demo limits reproducibility.
Limitations
The authors note several limitations, including the difficulty in making fair comparisons between codec-based and mel-spectrogram-based methods due to differences in representation mapping. Additionally, while the joint optimization framework is promising, the paper does not explore its application to other speech tasks beyond TTS and STT. The potential for overfitting or collapsing solutions in the discrete latent space is also mentioned, although not observed in their experiments.
Broader impact
The proposed model has significant implications for real-world applications in speech synthesis and recognition, particularly in enhancing the quality and efficiency of TTS systems. The ability to jointly model TTS and STT tasks could streamline workflows in various applications, such as virtual assistants and automated transcription services. However, ethical considerations regarding the misuse of speech generation technologies, such as voice cloning, must be addressed to ensure responsible use. The main contribution of this work is the introduction of MELD, a joint optimization framework for speech language modeling that effectively integrates discrete latent variables to enhance TTS and STT performance. This approach represents a significant advancement in the field, addressing key limitations of existing methods and paving the way for future research in multimodal speech processing.
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