2025/06/04 by Reo Yoneyama, Yoneyama, Reo, Masaya Kawamura +7
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Natural Language Processing Techniques #Parallel Computing and Optimization Techniques #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2506.03554
openalex publication_date 2025/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In real-time speech synthesis, neural vocoders often require low-latency synthesis through causal processing and streaming. However, streaming introduces inefficiencies absent in batch synthesis, such as limited parallelism, inter-frame dependency management, and parameter loading overhead. This paper proposes multi-stream Wavehax (MS-Wavehax), an efficient neural vocoder for low-latency streaming, by extending the aliasing-free neural vocoder Wavehax with multi-stream decomposition. We analyze the latency-throughput trade-off in a CPU-only environment and identify key bottlenecks in streaming neural vocoders. Our findings provide practical insights for optimizing chunk sizes and designing vocoders tailored to specific application demands and hardware constraints. Furthermore, our subjective evaluations show that MS-Wavehax delivers high speech quality under causal and non-causal conditions while being remarkably compact and easily deployable in resource-constrained environments.