Categories: FAANG

Improving the Quality of Neural TTS Using Long-form Content and Multi-speaker Multi-style Modeling

Neural text-to-speech (TTS) can provide quality close to natural speech if an adequate amount of high-quality speech material is available for training. However, acquiring speech data for TTS training is costly and time-consuming, especially if the goal is to generate different speaking styles. In this work, we show that we can transfer speaking style across speakers and improve the quality of synthetic speech by training a multi-speaker multi-style (MSMS) model with long-form recordings, in addition to regular TTS recordings. In particular, we show that 1) multi-speaker modeling improves the…

Compact Neural TTS Voices for Accessibility

Contemporary text-to-speech solutions for accessibility applications can typically be classified into two categories: (i) device-based statistical parametric speech synthesis (SPSS) or unit selection (USEL) and (ii) cloud-based neural TTS. SPSS and USEL offer low latency and low disk footprint at the expense of naturalness and audio quality. Cloud-based neural TTS…

January 31, 2025

In "FAANG"

Emphasis Control for Parallel Neural TTS

Recent parallel neural text-to-speech (TTS) synthesis methods are able to generate speech with high fidelity while maintaining high performance. However, these systems often lack control over the output prosody, thus restricting the semantic information conveyable for a given text. This paper proposes a hierarchical parallel neural TTS system for prosodic…

September 9, 2022

In "FAANG"

Speech AI Year in Review

Almost anywhere you looked, AI-based speech technologies continued to blossom in 2022, from increased interest measured in Google Trends, to surprising medical advances that suggest speech patterns can help detect some illnesses, to the variety of digital services and devices that users control with their voices. At Google Cloud, we spent…

December 17, 2022

In "FAANG"

AI Generated Robotic Content