Model training using parallel data with mismatched pause positions in statistical esophageal speech enhancement

Kishimoto, Mayumi; Toda, Tomoki; Doi, Hironori; Sakti, Sakriani; Nakamura, Satoshi

doi:10.1109/icosp.2012.6491557

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before training the LSTM network, it is necessary to align the source and target utterances. Due to the characteristics of the oesophageal speech, there is a very important mismatch between both healthy and oesophageal signals which causes the inadequacy of using a dynamic time warping (DTW) algorithm directly [31]. This is why both signals were labelled at phone level, and then the iterative alignment procedure described in [32] and [33] was applied for each pair of oesophageal and healthy phones.…”

Section: Spectral Conversionmentioning

confidence: 99%

LSTM based voice conversion for laryngectomees

Serrano¹,

Tavarez²,

Sarasola³

et al. 2018

IberSPEECH 2018

View full text Add to dashboard Cite

This paper describes a voice conversion system designed with the aim of improving the intelligibility and pleasantness of oesophageal voices. Two different systems have been built, one to transform the spectral magnitude and another one for the fundamental frequency, both based on DNNs. Ahocoder has been used to extract the spectral information (mel cepstral coefficients) and a specific pitch extractor has been developed to calculate the fundamental frequency of the oesophageal voices. The cepstral coefficients are converted by means of an LSTM network. The conversion of the intonation curve is implemented through two different LSTM networks, one dedicated to the voiced unvoiced detection and another one for the prediction of F0 from the converted cepstral coefficients. The experiments described here involve conversion from one oesophageal speaker to a specific healthy voice. The intelligibility of the signals has been measured with a Kaldi based ASR system. A preference test has been implemented to evaluate the subjective preference of the obtained converted voices comparing them with the original oesophageal voice. The results show that spectral conversion improves ASR while restoring the intonation is preferred by human listeners.

show abstract

Section: Spectral Conversionmentioning

confidence: 99%

LSTM based voice conversion for laryngectomees

Serrano¹,

Tavarez²,

Sarasola³

et al. 2018

IberSPEECH 2018

View full text Add to dashboard Cite

show abstract

“…Although non-parallel training is also possible [20], in VC, a set of parallel source-target sentences is desirable. A set of 50 phonemically balanced sentences in Japanese were used to evaluate the performance and capability of the different VC strategies that were aimed at improving the quality and intelligibility of alaryngeal voices [21] [22][23] [24][25] [26] [27].…”

Section: Introductionmentioning

confidence: 99%