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arXiv · 2111.09052

High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency

Nikolaos Ellinas·Georgios Vamvoukakis·Konstantinos Markopoulos·Aimilios Chalamandaris·Georgia Maniati·Panos Kakoulidis·Spyros Raptis·June Sig Sung·Hyoungmin Park·Pirros Tsiakoulis
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Abstract

This paper presents an end-to-end text-to-speech system with low latency on a CPU, suitable for real-time applications. The system is composed of an autoregressive attention-based sequence-to-sequence acoustic model and the LPCNet vocoder for waveform generation. An acoustic model architecture that adopts modules from both the Tacotron 1 and 2 models is proposed, while stability is ensured by using a recently proposed purely location-based attention mechanism, suitable for arbitrary sentence length generation. During inference, the decoder is unrolled and acoustic feature generation is performed in a streaming manner, allowing for a nearly constant latency which is independent from the sentence length. Experimental results show that the acoustic model can produce feature sequences with minimal latency about 31 times faster than real-time on a computer CPU and 6.5 times on a mobile CPU, enabling it to meet the conditions required for real-time applications on both devices. The full end-to-end system can generate almost natural quality speech, which is verified by listening tests.

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Nikolaos Ellinas, Georgios Vamvoukakis, Konstantinos Markopoulos, Aimilios Chalamandaris, Georgia Maniati, Panos Kakoulidis, Spyros Raptis, June Sig Sung, Hyoungmin Park, Pirros Tsiakoulis. 2021-11-17. High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency. https://doi.org/10.21437/interspeech.2020-2464

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