1,525 research outputs found
Formula of Entropy along Unstable Foliations for Diffeomorphisms with Dominated Splitting
Metric entropies along a hierarchy of unstable foliations are investigated
for diffeomorphisms with dominated splitting. The analogues of Ruelle's
inequality and Pesin's formula, which relate the metric entropy and Lyapunov
exponents in each hierarchy, are given
RawNet: Fast End-to-End Neural Vocoder
Neural networks based vocoders have recently demonstrated the powerful
ability to synthesize high quality speech. These models usually generate
samples by conditioning on some spectrum features, such as Mel-spectrum.
However, these features are extracted by using speech analysis module including
some processing based on the human knowledge. In this work, we proposed RawNet,
a truly end-to-end neural vocoder, which use a coder network to learn the
higher representation of signal, and an autoregressive voder network to
generate speech sample by sample. The coder and voder together act like an
auto-encoder network, and could be jointly trained directly on raw waveform
without any human-designed features. The experiments on the Copy-Synthesis
tasks show that RawNet can achieve the comparative synthesized speech quality
with LPCNet, with a smaller model architecture and faster speech generation at
the inference step.Comment: Submitted to Interspeech 2019, Graz, Austri
Attention-Based End-to-End Speech Recognition on Voice Search
Recently, there has been a growing interest in end-to-end speech recognition
that directly transcribes speech to text without any predefined alignments. In
this paper, we explore the use of attention-based encoder-decoder model for
Mandarin speech recognition on a voice search task. Previous attempts have
shown that applying attention-based encoder-decoder to Mandarin speech
recognition was quite difficult due to the logographic orthography of Mandarin,
the large vocabulary and the conditional dependency of the attention model. In
this paper, we use character embedding to deal with the large vocabulary.
Several tricks are used for effective model training, including L2
regularization, Gaussian weight noise and frame skipping. We compare two
attention mechanisms and use attention smoothing to cover long context in the
attention model. Taken together, these tricks allow us to finally achieve a
character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on
the MiTV voice search dataset. While together with a trigram language model,
CER and SER reach 2.81% and 5.77%, respectively
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