A Spanish multispeaker database of esophageal speech

García, Luis Serrano; Raman, Sneha; Rioja, Inma Hernáez; Navas, Eva; Sánchez, Jon; Saratxaga, Ibon

doi:10.1016/j.csl.2020.101168

Cited by 2 publications

(3 citation statements)

References 43 publications

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“…OS is less intelligible and more effortful to listen to compared to healthy speech (HS). This is evident from previous listening experiments [10,11] as well as acoustic characteristics and challenges of OS [12]. Prolonged exposure to effortful speech causes fatigue in listeners [13].…”

Section: Introductionmentioning

confidence: 74%

“…We chose four OS speakers (3 male, 1 female) with a wide range of intelligibility from an OS database that contains over 30 OS speakers [12]. In the original database, the four speakers were identified as '02M3', '04M3', '16M3' and '25F3', and we continue to use these IDs.…”

Section: Datamentioning

confidence: 99%

“…For each speaker, we used a parallel dataset of 100 phonetically-balanced Spanish sentences (described in detail in [12]). The sentences were syntactically and semantically predictable but had some low frequency words.…”

Section: Datamentioning

confidence: 99%

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Enrichment of Oesophageal Speech: Voice Conversion with Duration–Matched Synthetic Speech as Target

et al. 2021

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Pathological speech such as Oesophageal Speech (OS) is difficult to understand due to the presence of undesired artefacts and lack of normal healthy speech characteristics. Modern speech technologies and machine learning enable us to transform pathological speech to improve intelligibility and quality. We have used a neural network based voice conversion method with the aim of improving the intelligibility and reducing the listening effort (LE) of four OS speakers of varying speaking proficiency. The novelty of this method is the use of synthetic speech matched in duration with the source OS as the target, instead of parallel aligned healthy speech. We evaluated the converted samples from this system using a collection of Automatic Speech Recognition systems (ASR), an objective intelligibility metric (STOI) and a subjective test. ASR evaluation shows that the proposed system had significantly better word recognition accuracy compared to unprocessed OS, and baseline systems which used aligned healthy speech as the target. There was an improvement of at least 15% on STOI scores indicating a higher intelligibility for the proposed system compared to unprocessed OS, and a higher target similarity in the proposed system compared to baseline systems. The subjective test reveals a significant preference for the proposed system compared to unprocessed OS for all OS speakers, except one who was the least proficient OS speaker in the data set.

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