Incorporation of Happiness in Neutral Speech by Modifying Time-Domain Parameters of Emotive-Keywords

Gladston, Anushiya Rachel; Sreenidhi, S.; Vijayalakshmi, P.; Nagarajan, T.

doi:10.1007/s00034-021-01875-7

Cited by 1 publication

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References 22 publications

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“…The text-to-speech (TTS) technique has played a vital role in interaction platforms to help generate natural and human-like speech based on input texts, especially for people with vision impairment [1][2][3]. It can serve as an assistive tool in many areas such as language education, voice navigation, clinical rehabilitation, and recreation [4,5].…”

Section: Introductionmentioning

confidence: 99%

DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer

et al. 2022

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Text-to-speech (TTS) synthesizers have been widely used as a vital assistive tool in various fields. Traditional sequence-to-sequence (seq2seq) TTS such as Tacotron2 uses a single soft attention mechanism for encoder and decoder alignment tasks, which is the biggest shortcoming that incorrectly or repeatedly generates words when dealing with long sentences. It may also generate sentences with run-on and wrong breaks regardless of punctuation marks, which causes the synthesized waveform to lack emotion and sound unnatural. In this paper, we propose an end-to-end neural generative TTS model that is based on the deep-inherited attention (DIA) mechanism along with an adjustable local-sensitive factor (LSF). The inheritance mechanism allows multiple iterations of the DIA by sharing the same training parameter, which tightens the token–frame correlation, as well as fastens the alignment process. In addition, LSF is adopted to enhance the context connection by expanding the DIA concentration region. In addition, a multi-RNN block is used in the decoder for better acoustic feature extraction and generation. Hidden-state information driven from the multi-RNN layers is utilized for attention alignment. The collaborative work of the DIA and multi-RNN layers contributes to outperformance in the high-quality prediction of the phrase breaks of the synthesized speech. We used WaveGlow as a vocoder for real-time, human-like audio synthesis. Human subjective experiments show that the DIA-TTS achieved a mean opinion score (MOS) of 4.48 in terms of naturalness. Ablation studies further prove the superiority of the DIA mechanism for the enhancement of phrase breaks and attention robustness.

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Section: Introductionmentioning

confidence: 99%

DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer

et al. 2022

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View full text Add to dashboard Cite

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Incorporation of Happiness in Neutral Speech by Modifying Time-Domain Parameters of Emotive-Keywords

Cited by 1 publication

References 22 publications

DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer

DIA-TTS: Deep-Inherited Attention-Based Text-to-Speech Synthesizer

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