ClariCodec: Optimising Neural Speech Codes for 200bps Communication using Reinforcement Learning
Abstract
In bandwidth-constrained communication such as satellite and underwater channels, speech must often be transmitted at ultra-low bitrates where intelligibility is the primary objective. At such extreme compression levels, codecs trained with acoustic reconstruction losses tend to allocate bits to perceptual detail, leading to substantial degradation in word error rate (WER). This paper proposes ClariCodec, a neural speech codec operating at 200 bit per second (bps) that reformulates quantisation as a stochastic policy, enabling reinforcement learning (RL)-based optimisation of intelligibility. Specifically, the encoder is fine-tuned using WER-driven rewards while the acoustic reconstruction pipeline remains frozen. Even without RL, ClariCodec achieves 3.68% WER on the LibriSpeech test-clean set at 200 bps, already competitive with codecs operating at higher bitrates. Further RL fine-tuning reduces WER to 3.20% on test-clean and 8.93% on test-other, corresponding to a 13% relative reduction while preserving perceptual quality.
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
ClariCodec presents a novel approach to neural speech coding by optimising for intelligibility at ultra-low bitrates using reinforcement learning. This work significantly advances the state of the art in speech codecs, addressing critical challenges in bandwidth-constrained communication environments while maintaining competitive performance metrics.
Methodology
The methodology proposed in ClariCodec is innovative, particularly in its two-stage training approach that combines traditional reconstruction-based training with reinforcement learning (RL) for semantic optimisation. The reformulation of quantisation as a stochastic policy is a significant advancement, allowing for the direct optimisation of intelligibility using word error rate (WER) as a reward signal. This novel approach addresses the limitations of existing codecs that prioritize acoustic fidelity over intelligibility, making it a meaningful contribution to the field of neural speech coding.
Results & evaluation
The experimental evaluation is robust, utilizing the LibriSpeech dataset to benchmark performance against several existing neural speech codecs. The results demonstrate that ClariCodec achieves competitive performance at an unprecedented low bitrate of 200 bps, with a WER of 3.20% on test-clean and 8.93% on test-other. The paper includes comprehensive comparisons with baseline models, showing that ClariCodec maintains perceptual quality while achieving significant improvements in intelligibility through RL fine-tuning.
Reproducibility
The paper provides detailed implementation information, including model architecture, training setup, and loss functions used in both stages of training. However, the lack of a publicly available code repository limits the reproducibility of the results. The authors mention using specific hardware and configurations, which could aid in reproducing the experiments if the code were available.
Limitations
One limitation noted is the potential degradation in acoustic quality when optimising solely for intelligibility during the RL fine-tuning phase. The paper addresses this by incorporating a mel reconstruction loss to mitigate quality loss, but this trade-off remains a concern. Additionally, the non-causal architecture may introduce latency issues, which the authors plan to address in future work.
Broader impact
The implications of ClariCodec are significant, particularly for applications in bandwidth-constrained environments such as satellite and underwater communication. By prioritising intelligibility over acoustic fidelity, this codec could enhance communication reliability in critical scenarios. The potential for future developments, such as streaming codecs and integration with generative tasks, suggests a broad range of applications in speech technology. ClariCodec presents a novel approach to neural speech coding by optimising for intelligibility at ultra-low bitrates using reinforcement learning. This work significantly advances the state of the art in speech codecs, addressing critical challenges in bandwidth-constrained communication environments while maintaining competitive performance metrics.
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