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Multimodal Prediction of Affective Dimensions via Fusing Multiple Regression Techniques.

, , , , , , и . INTERSPEECH, стр. 162-165. ISCA, (2017)

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Multimodal Prediction of Affective Dimensions via Fusing Multiple Regression Techniques., , , , , , и . INTERSPEECH, стр. 162-165. ISCA, (2017)A light-weight method of building an LSTM-RNN-based bilingual tts system., , , и . IALP, стр. 201-205. IEEE, (2017)Learning optimal features for music transcription., , , и . ChinaSIP, стр. 105-109. IEEE, (2014)Mapping frames with DNN-HMM recognizer for non-parallel voice conversion., , , , , , , , и . APSIPA, стр. 488-494. IEEE, (2015)Exemplar-based sparse representation of timbre and prosody for voice conversion., , , , , и . ICASSP, стр. 5175-5179. IEEE, (2016)An Automatic Voice Conversion Evaluation Strategy Based on Perceptual Background Noise Distortion and Speaker Similarity., , , , , , , , , и 2 other автор(ы). SSW, стр. 44-51. ISCA, (2016)s-Transformer: Segment-Transformer for Robust Neural Speech Synthesis., , , и . CoRR, (2020)Improving Prosody with Linguistic and Bert Derived Features in Multi-Speaker Based Mandarin Chinese Neural TTS., , , и . ICASSP, стр. 6704-6708. IEEE, (2020)Deep neural network derived bottleneck features for accurate audio classification., , , , , и . ICME Workshops, стр. 1-6. IEEE Computer Society, (2016)Deep Bidirectional LSTM Modeling of Timbre and Prosody for Emotional Voice Conversion., , , , , и . INTERSPEECH, стр. 2453-2457. ISCA, (2016)