TokenChain: A Discrete Speech Chain via Semantic Token Modeling
Abstract
Machine Speech Chain, simulating the human perception-production loop, proves effective in jointly improving ASR and TTS. We propose TokenChain, a fully discrete speech chain coupling semantic-token ASR with a two-stage TTS: an autoregressive text-to-semantic model co-trained with ASR and a masked-generative semantic-to-acoustic model for synthesis only. End-to-end feedback across the text interface is enabled with straight-through argmax/Gumbel-Softmax and balanced with supervised ASR via dynamic weight averaging. Ablations examine optimal temperature schedules for in- and cross-domain transfer. Evaluation reveals TokenChain surpasses baseline accuracy 2-6 epochs earlier and yields 5-13% lower equal-epoch error with stable T2S on LibriSpeech, and reduces relative ASR WER by 56% and T2S WER by 31% on TED-LIUM with minimal forgetting, showing that chain learning remains effective with token interfaces and models.
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Research summary
TokenChain introduces a novel discrete speech chain framework that effectively integrates semantic-token ASR with a two-stage TTS system, demonstrating significant improvements in performance and convergence. This work represents a meaningful advancement in the field of speech processing, with potential applications across various domains.
Methodology
The methodology presented in TokenChain is innovative, leveraging a fully discrete speech chain that integrates semantic-token ASR with a two-stage TTS system. The authors employ advanced techniques such as straight-through estimators and Gumbel-Softmax to facilitate end-to-end feedback, which is a significant improvement over traditional continuous intermediate approaches. The dynamic weight averaging for balancing the ASR and TTS components is a noteworthy addition that enhances the training process.
Results & evaluation
The experimental evaluation is rigorous, utilizing well-established datasets such as LibriSpeech and TED-LIUM. The results demonstrate that TokenChain surpasses baseline models in terms of accuracy and convergence speed, achieving improvements in word error rates (WER) and character error rates (CER). The ablation studies on temperature schedules for in- and cross-domain transfer further strengthen the findings, showcasing a comprehensive approach to model evaluation.
Reproducibility
The paper provides sufficient details regarding the model architecture, training procedures, and evaluation metrics, which would allow other researchers to replicate the experiments. However, the absence of a public code repository or demo URL limits the ease of reproducibility.
Limitations
One limitation is the reliance on specific datasets, which may not generalize across all speech recognition and synthesis tasks. Additionally, the paper does not address potential computational overheads associated with the two-stage TTS system, which could affect real-time applications.
Broader impact
The implications of this work are significant for the fields of automatic speech recognition and text-to-speech synthesis, particularly in enhancing the efficiency and effectiveness of machine speech systems. The approach could lead to more robust applications in voice assistants, accessibility tools, and language learning technologies. TokenChain introduces a novel discrete speech chain framework that effectively integrates semantic-token ASR with a two-stage TTS system, demonstrating significant improvements in performance and convergence. This work represents a meaningful advancement in the field of speech processing, with potential applications across various domains.
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