# A Distilled Low-Latency Neural Vocoder with Explicit Amplitude and Phase Prediction

arXiv: https://arxiv.org/abs/2509.13667 · HTML: https://mlsanity.com/paper/2509.13667/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [Paradee: Distilling Kokoro-82M into an 8M-Parameter Single-Voice Text-to-Speech Model](https://mlsanity.com/paper/2610.06817/) (2026)

## Abstract (arXiv)

The majority of mainstream neural vocoders primarily focus on speech quality and generation speed, while overlooking latency, which is a critical factor in real-time applications. Excessive latency leads to noticeable delays in user interaction, severely degrading the user experience and rendering such systems impractical for real-time use. Therefore, this paper proposes DLL-APNet, a Distilled Low-Latency neural vocoder which first predicts the Amplitude and Phase spectra explicitly from input mel spectrogram and then reconstructs the speech waveform via inverse short-time Fourier transform (iSTFT). The DLL-APNet vocoder leverages causal convolutions to constrain the utilization of information to current and historical contexts, effectively minimizing latency. To mitigate speech quality degradation caused by causal constraints, a knowledge distillation strategy is proposed, where a pre-trained non-causal teacher vocoder guides intermediate feature generation of the causal student DLL-APNet vocoder. Experimental results demonstrate that the proposed DLL-APNet vocoder produces higher-quality speech than other causal vocoders, while requiring fewer computational resources. Furthermore, the proposed DLL-APNet vocoder achieves speech quality on par with mainstream non-causal neural vocoders, validating its ability to deliver both high perceptual quality and low latency.