A Lyrics to Singing Voice Synthesis System with Variable Timbre

Li, Jinlong; Yang, Hongwu; Zhang, Weizhao; Cai, Lianhong

doi:10.1007/978-3-642-23220-6_23

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Some works used the system to get a spoken version of the song and transform it into singing by incorporating a signal processing stage. For instance, in [22], the synthetic speech was converted into singing according to a MIDI file input, using STRAIGHT to perform the analysis, transformation and synthesis. In [17], an HMM-based TTS synthesiser for Basque was used to generate a singing voice.…”

Section: Singing Synthesismentioning

confidence: 99%

“…The audios generated for one of the five scores have been provided as Additional files 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23, and 24. Forty-nine Spanish native speakers took part in the test.…”

Section: Subjective Evaluation 431 Mushra Test Setupmentioning

confidence: 99%

“…As an alternative, we could take advantage of those approaches which focus on the production of singing from speech following the so-called speechto-singing (STS) conversion [19][20][21]. These techniques can be applied to the output of a TTS system to transform speech to singing by maintaining the identity of the speaker [18,22]. However, this straightforward approach has been proved suboptimal in terms of flexibility and computational costs [18].…”

Section: Introductionmentioning

confidence: 99%

“…(2019) 2019: 22 Page 13 of 14 ting vibrato to the singing expression control generation module. Furthermore, other signal-processing techniques could be considered for the transformation module to better cope with the challenge of generating singing from neutral speech.…”

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confidence: 99%

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A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

Freixes

Álías

Socoró

2019

J AUDIO SPEECH MUSIC PROC.

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Text-to-speech (TTS) synthesis systems have been widely used in general-purpose applications based on the generation of speech. Nonetheless, there are some domains, such as storytelling or voice output aid devices, which may also require singing. To enable a corpus-based TTS system to sing, a supplementary singing database should be recorded. This solution, however, might be too costly for eventual singing needs, or even unfeasible if the original speaker is unavailable or unable to sing properly. This work introduces a unit selection-based text-to-speech-and-singing (US-TTS&S) synthesis framework, which integrates speech-to-singing (STS) conversion to enable the generation of both speech and singing from an input text and a score, respectively, using the same neutral speech corpus. The viability of the proposal is evaluated considering three vocal ranges and two tempos on a proof-of-concept implementation using a 2.6-h Spanish neutral speech corpus. The experiments show that challenging STS transformation factors are required to sing beyond the corpus vocal range and/or with notes longer than 150 ms. While score-driven US configurations allow the reduction of pitch-scale factors, timescale factors are not reduced due to the short length of the spoken vowels. Moreover, in the MUSHRA test, text-driven and score-driven US configurations obtain similar naturalness rates of around 40 for all the analysed scenarios. Although these naturalness scores are far from those of vocaloid, the singing scores of around 60 which were obtained validate that the framework could reasonably address eventual singing needs.

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Section: Singing Synthesismentioning

confidence: 99%

Section: Subjective Evaluation 431 Mushra Test Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

Freixes

Álías

Socoró

2019

J AUDIO SPEECH MUSIC PROC.

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“…In recent years, speech synthesis techniques have been well developed; e.g., text reading system, speech-oriented guidance system, and synthesizing singing voices [1][2][3]. However, in these systems only the linguistic informat ion is synthesized, and they cannot handle human emotion.…”

Section: Introductionmentioning

confidence: 99%

GMM-Based Emotional Voice Conversion Using Spectrum and Prosody Features

Aihara

Takashima²,

Takiguchi³

et al. 2012

AJSP

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We propose Gaussian Mixture Model (GMM)-based emotional voice conversion using spectrum and prosody features. In recent years, speech recognition and synthesis techniques have been developed, and an emotional voice conversion technique is required for synthesizing more exp ressive voices. The common emotional conversion was based on transformation of neutral prosody to emotional prosody by using huge speech corpus. In this paper, we convert a neutral voice to an emot ional vo ice using GMMs. GMM-based spectrum conversion is widely used to modify non linguistic informat ion such as voice characteristics while keep ing linguistic information unchanged. Because the conventional method converts either prosody or voice quality (spectrum), so me emot ions are not converted well. In our method, both prosody and voice quality are used for converting a neutral voice to an emotional voice, and it is able to obtain more expressive voices in comparison with conventional methods, such as prosody or spectrum conversion.

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Multi-Voice Singing Synthesis From Lyrics

Resna

Rajan

2022

Circuits Syst Signal Process

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A Lyrics to Singing Voice Synthesis System with Variable Timbre

Cited by 6 publications

References 6 publications

A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

A unit selection text-to-speech-and-singing synthesis framework from neutral speech: proof of concept

GMM-Based Emotional Voice Conversion Using Spectrum and Prosody Features

Multi-Voice Singing Synthesis From Lyrics

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