An evaluation of alaryngeal speech enhancement methods based on voice conversion techniques

Doi, Hironori; Nakamura, Keigo; Toda, Tomoki; Saruwatari, Hiroshi; Shikano, Kiyohiro

doi:10.1109/icassp.2011.5947513

Cited by 18 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…VC research was originally started to develop a speaker individuality conversion technique enabling a source speaker to change his/her speaker individuality to that of another target speaker while preserving the linguistic content [2]. The conversion frameworks of VC have been adopted into other research objectives such as speech recovery for people with speech disorders [3], singing style conversion [4], nonnative to native speaker conversion [5], and speech to articula-tory mapping [6] to make it possible to implement augmented speech communications. Towards the practical use of these VC applications, it is essential to improve fundamental VC techniques.…”

Section: Introductionmentioning

confidence: 99%

sprocket: Open-Source Voice Conversion Software

Kobayashi¹,

Toda²

2018

EasyChair Preprints

Self Cite

View full text Add to dashboard Cite

Statistical voice conversion (VC) is a technique to convert specific non-or paralinguistic information while keeping linguistic information unchanged, and speaker conversion has been studied as a typical application of VC for a few decades. To better understand various VC techniques using a freely available common dataset, the Voice Conversion Challenge (VCC) was launched in 2016 and the 2nd challenge was held in 2018. As one of the baseline systems for VCC 2018, we developed open-source VC software called "sprocket", in which not only conventional techniques, such as a trajectory-based conversion method using a Gaussian mixture model (GMM) and a vocoderbased conversion framework but also recently developed techniques, such as a vocoder-free VC framework, have been implemented. Using sprocket, it is possible to 1) easily reproduce converted voices using the VCC datasets and 2) develop VC systems using other parallel speech datasets with fundamental VC functions, such as acoustic feature extraction, time alignment between the source and target features, GMM training, feature conversion, and waveform generation. In this paper, we describe 1) the technical details and use of sprocket, 2) the development of the baseline systems for the HUB and SPOKE tasks of VCC 2018 using sprocket, and 3) the performance of sprocket as a VC system by demonstrating results for our developed baseline systems in VCC 2018.

show abstract

Section: Introductionmentioning

confidence: 99%

sprocket: Open-Source Voice Conversion Software

Kobayashi¹,

Toda²

2018

EasyChair Preprints

Self Cite

View full text Add to dashboard Cite

show abstract

“…We recruited more than 10 native Mandarin speakers as participants. Audio samples can be found online 2 .…”

Section: Subjective Evaluationmentioning

confidence: 99%

Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion

Yi-Syuan¹,

Huang²,

Yen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Voice conversion (VC) is an effective approach to electrolaryngeal (EL) speech enhancement, a task that aims to improve the quality of the artificial voice from an electrolarynx device. In frame-based VC methods, time alignment needs to be performed prior to model training, and the dynamic time warping (DTW) algorithm is widely adopted to compute the best time alignment between each utterance pair. The validity is based on the assumption that the same phonemes of the speakers have similar features and can be mapped by measuring a predefined distance between speech frames of the source and the target. However, the special characteristics of the EL speech can break the assumption, resulting in a sub-optimal DTW alignment. In this work, we propose to use lip images for time alignment, as we assume that the lip movements of laryngectomee remain normal compared to healthy people. We investigate two naive lip representations and distance metrics, and experimental results demonstrate that the proposed method can significantly outperform the audio-only alignment in terms of objective and subjective evaluations.

show abstract

“…La voz producida por medio de este método se conoce como voz esofágica (que no es producida en la laringe). La utilización del habla sub-vocal en pacientes que han sido sometidos a una laringectomía total se presenta en [49]. El reconocimiento de la voz no laríngea y el incremento de la calidad de las señales producidas por una laringe electrónica usando métodos de reconocimiento de patrones son temas estudiados en [50].…”

Section: Aplicaciones Actuales Del Habla Sub-vocalunclassified

Revisión de las Tecnologías y Aplicaciones del Habla Sub-vocal

Gamma

Amaya

Sandoval

2015

Ing.

View full text Add to dashboard Cite

Este trabajo presenta una revisión de estado de las principales temáticas aplicativas y metodológicas del habla sub-vocal que se han venido desarrollando en los últimos años. La primera sección hace una honda revisión de los métodos de detección del lenguaje silencioso. En la segunda parte se evalúan las tecnologías implementadas en los últimos años, seguido de un análisis en las principales aplicaciones de este tipo de lenguaje y finalmente presentado una amplia comparación entre los trabajos que se han hecho en industria y academia utilizando este tipo de desarrollos.

show abstract

An evaluation of alaryngeal speech enhancement methods based on voice conversion techniques

Cited by 18 publications

References 9 publications

sprocket: Open-Source Voice Conversion Software

sprocket: Open-Source Voice Conversion Software

Time Alignment using Lip Images for Frame-based Electrolaryngeal Voice Conversion

Revisión de las Tecnologías y Aplicaciones del Habla Sub-vocal

Contact Info

Product

Resources

About