A neural network model that significant improves unitselection-based Text-To-Speech synthesis is presented. The model employs a sequence-to-sequence LSTM-based autoencoder that compresses the acoustic and linguistic features of each unit to a fixed-size vector referred to as an embedding. Unit-selection is facilitated by formulating the target cost as an L2 distance in the embedding space. In open-domain speech synthesis the method achieves a 0.2 improvement in the MOS, while for limited-domain it reaches the cap of 4.5 MOS. Furthermore, the new TTS system halves the gap between the previous unit-selection system and WaveNet in terms of quality while retaining low computational cost and latency.
This paper presents improvements to an automatic dubbing system in which text-to-speech technology is used to synthesise speech from subtitles. Spring-based subtitle timing optimisation was proposed to reduce the need for speeding up synthetic speech to fit it into corresponding subtitle slots. Video cut detection algorithm was also introduced, and the cuts were then used to prevent stretching subtitles across the cuts. Results show that after the optimisation smaller speeding-up factors are applied on synthetic speech while keeping optimised subtitle start and end times close to original positions.
We introduce a unified Grapheme-to-phoneme conversion framework based on the composition of deep neural networks. In contrary to the usual approaches building the G2P frameworks from the dictionary, we use whole phrases, which allows us to capture various language properties, e.g. crossword assimilation, without the need for any special care or topology adjustments. The evaluation is carried out on three different languages-English, Czech and Russian. Each requires dealing with specific properties, stressing the proposed framework in various ways. The very first results show promising performance of the proposed framework, dealing with all the phenomena specific to the tested languages. Thus, we consider the framework to be language-independent for a wide range of languages.
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