
- Read more about DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS
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Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional postfilters that are widely used in SPSS systems for increasing the quality of synthesized speech. However, existing DNN-based postfilters are trained with speaker-dependent databases. Given that SPSS systems can rapidly adapt to new speakers from generic models, there is a need for DNN-based postfilters that can adapt to new speakers with minimal adaptation data.
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- Read more about F0 CONTOUR ESTIMATION USING PHONETIC FEATURE IN ELECTROLARYNGEAL SPEECH ENHANCEMENT
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Pitch plays a significant role in understanding a tone based language like Mandarin. In this paper, we present a new method that estimates F0 contour for electrolaryngeal (EL) speech enhancement in Mandarin. Our system explores the usage of phonetic feature to improve the quality of EL speech. First, we train an acoustic model for EL speech and generate the phoneme posterior probabilities feature sequence for each input EL speech utterance. Then we employ the phonetic feature for F0 contour generation rather than the acoustic feature.
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- Read more about Investigating the Effect of Sound-Event Loudness on Crowdsourced Audio Annotations
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Audio annotation is an important step in developing machine-listening systems. It is also a time consuming process, which has motivated investigators to crowdsource audio annotations. However, there are many factors that affect annotations, many of which have not been adequately investigated. In previous work, we investigated the effects of visualization aids and sound scene complexity on the quality of crowdsourced sound-event annotations.
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- Read more about SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS
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This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information.
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- Read more about SAMPLERNN-BASED NEURAL VOCODER FOR STATISTICAL PARAMETRIC SPEECH SYNTHESIS
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This paper presents a SampleRNN-based neural vocoder for statistical parametric speech synthesis. This method utilizes a conditional SampleRNN model composed of a hierarchical structure of GRU layers and feed-forward layers to capture long-span dependencies between acoustic features and waveform sequences. Compared with conventional vocoders based on the source-filter model, our proposed vocoder is trained without assumptions derived from the prior knowledge of speech production and is able to provide a better modeling and recovery of phase information.
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- Read more about REVISITING THE PROBLEM OF AUDIO-BASED HIT SONG PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS
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Being able to predict whether a song can be a hit has important applications in the music industry. Although it is true that the popularity of a song can be greatly affected by external factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own.
icassp2017.pdf

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- Read more about Global Variance in Speech Synthesis with Linear Dynamical Models
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GV_LDM.pdf

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- Read more about poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK
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- Read more about poster_STEGANALYSIS OF AAC USINGCALIBRATED MARKOV MODEL OF ADJACENT CODEBOOK
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