Exploration of End-to-End Synthesisers for Zero Resource Speech Challenge 2020
| Autor: | M Mano Ranjith Kumar, Anusha Prakash, Karthik Pandia D S, Hema A. Murthy |
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| Rok vydání: | 2020 |
| Předmět: |
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Computer science Speech recognition Speech synthesis 02 engineering and technology computer.software_genre 01 natural sciences Zero (linguistics) Set (abstract data type) Task (computing) Resource (project management) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Spectrogram 020201 artificial intelligence & image processing Transient (computer programming) 010301 acoustics computer |
| Zdroj: | INTERSPEECH |
| DOI: | 10.21437/interspeech.2020-2731 |
| Popis: | A Spoken dialogue system for an unseen language is referred to as Zero resource speech. It is especially beneficial for developing applications for languages that have low digital resources. Zero resource speech synthesis is the task of building text-to-speech (TTS) models in the absence of transcriptions. In this work, speech is modelled as a sequence of transient and steady-state acoustic units, and a unique set of acoustic units is discovered by iterative training. Using the acoustic unit sequence, TTS models are trained. The main goal of this work is to improve the synthesis quality of zero resource TTS system. Four different systems are proposed. All the systems consist of three stages: unit discovery, followed by unit sequence to spectrogram mapping, and finally spectrogram to speech inversion. Modifications are proposed to the spectrogram mapping stage. These modifications include training the mapping on voice data, using x-vectors to improve the mapping, two-stage learning, and gender-specific modelling. Evaluation of the proposed systems in the Zerospeech 2020 challenge shows that quite good quality synthesis can be achieved. |
| Databáze: | OpenAIRE |
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