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Victor Ungureanu

Victor Ungureanu

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    Real-time Speech Frequency Bandwidth Extension
    Dominik Roblek
    Oleg Rybakov
    2021 IEEE International Conference on Acoustics, Speech and Signal Processing (to appear)
    Preview abstract In this paper we propose a lightweight model that performs frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz, while restoring the high frequency content to a level that is indistinguishable from the original samples at 16kHz. The model architecture is based on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional model, which adopts a combination of feature losses and adversarial losses to reconstruct an enhanced version of the input speech. In addition, we propose a version of SEANet that can be deployed on device in streaming mode, achieving an architecture latency of 16ms. When profiled on a single mobile CPU, processing one 16ms frame takes only 1.5ms, so that the total latency is compatible with a deployment in bi-directional voice communication systems. View details
    Preview abstract Common text-to-speech (TTS) systems rely on training data for modelling human speech. The quality of this data can range from professional voice actors recording hand-curated sentences in high-quality studio conditions, to found voice data representing arbitrary domains. For years, the unit selection technology dominant in the field required many hours of data that was expensive and time-consuming to collect. With the advancement of statistical methods of waveform generation, there have been experiments with more noisy and often much larger datasets (“big data”), testing the inherent flexibility of such systems. In this paper we examine the relationship between training data and speech synthesis quality. We then hypothesise that statistical text-to-speech benefits from high acoustic quality corpora with high level of prosodic variation, but that beyond the first few hours of training data we don’t observe quality gains. We then describe how we engineered a training dataset containing optimized distribution of features, and how these features were defined. Lastly, we present results from a series of evaluation tests. These confirm our hypothesis and show how a carefully engineered training corpus of a smaller size yields the same speech quality as much larger datasets, particularly for voices that use WaveNet. View details
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