个性化神经语音编解码器
Personalized Neural Speech Codec
AI总结：
本文提出个性化神经语音编解码器，通过Siamese网络学习说话人嵌入并进行聚类，再为每个聚类训练专用LPCNet编解码器，在相同模型复杂度下提升语音质量或实现模型压缩。
摘要：
在本文中，我们提出了一种个性化神经语音编解码器，设想个性化可以降低模型复杂度或提高感知语音质量。尽管语音编解码器在通信两端通常只涉及单个说话人，但针对特定用户对编解码器进行个性化在文献中很少被探索。首先，我们假设可以根据感知相似性将说话人划分为较小的子集。然后，我们还假设针对特定群体的编解码器可以专注于该群体的语音特征，从而提高其感知质量和计算效率。为此，我们首先开发了一个Siamese网络，从LibriSpeech数据集中学习说话人嵌入，然后将这些嵌入分组为潜在的说话人聚类。最后，我们在每个说话人聚类上重新训练基于LPCNet的语音编解码器基线。主观听力测试表明，所提出的个性化方案在保持语音质量的同时实现了模型压缩。换句话说，在相同的模型复杂度下，个性化编解码器能够产生更好的语音质量。
英文摘要：
In this paper, we propose a personalized neural speech codec, envisioning that personalization can reduce the model complexity or improve perceptual speech quality. Despite the common usage of speech codecs where only a single talker is involved on each side of the communication, personalizing a codec for the specific user has rarely been explored in the literature. First, we assume speakers can be grouped into smaller subsets based on their perceptual similarity. Then, we also postulate that a group-specific codec can focus on the group's speech characteristics to improve its perceptual quality and computational efficiency. To this end, we first develop a Siamese network that learns the speaker embeddings from the LibriSpeech dataset, which are then grouped into underlying speaker clusters. Finally, we retrain the LPCNet-based speech codec baselines on each of the speaker clusters. Subjective listening tests show that the proposed personalization scheme introduces model compression while maintaining speech quality. In other words, with the same model complexity, personalized codecs produce better speech quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
