171 research outputs found
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages, such
as a text analysis frontend, an acoustic model and an audio synthesis module.
Building these components often requires extensive domain expertise and may
contain brittle design choices. In this paper, we present Tacotron, an
end-to-end generative text-to-speech model that synthesizes speech directly
from characters. Given pairs, the model can be trained completely
from scratch with random initialization. We present several key techniques to
make the sequence-to-sequence framework perform well for this challenging task.
Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English,
outperforming a production parametric system in terms of naturalness. In
addition, since Tacotron generates speech at the frame level, it's
substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be
consistent with our conference submission (no content change other than typo
fixes
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech
synthesis directly from text. The system is composed of a recurrent
sequence-to-sequence feature prediction network that maps character embeddings
to mel-scale spectrograms, followed by a modified WaveNet model acting as a
vocoder to synthesize timedomain waveforms from those spectrograms. Our model
achieves a mean opinion score (MOS) of comparable to a MOS of for
professionally recorded speech. To validate our design choices, we present
ablation studies of key components of our system and evaluate the impact of
using mel spectrograms as the input to WaveNet instead of linguistic, duration,
and features. We further demonstrate that using a compact acoustic
intermediate representation enables significant simplification of the WaveNet
architecture.Comment: Accepted to ICASSP 201
Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences
given input speech, are good candidates for on-device speech recognition. E2E
models, however, present numerous challenges: In order to be truly useful, such
models must decode speech utterances in a streaming fashion, in real time; they
must be robust to the long tail of use cases; they must be able to leverage
user-specific context (e.g., contact lists); and above all, they must be
extremely accurate. In this work, we describe our efforts at building an E2E
speech recognizer using a recurrent neural network transducer. In experimental
evaluations, we find that the proposed approach can outperform a conventional
CTC-based model in terms of both latency and accuracy in a number of evaluation
categories
Malay articulation system for early screening diagnostic using hidden markov model and genetic algorithm
Speech recognition is an important technology and can be used as a great aid for individuals with sight or hearing disabilities today. There are extensive research interest and development in this area for over the past decades. However, the prospect in Malaysia regarding the usage and exposure is still immature even though there is demand from the medical and healthcare sector. The aim of this research is to assess the quality and the impact of using computerized method for early screening of speech articulation disorder among Malaysian such as the omission, substitution, addition and distortion in their speech. In this study, the statistical probabilistic approach using Hidden Markov Model (HMM) has been adopted with newly designed Malay corpus for articulation disorder case following the SAMPA and IPA guidelines. Improvement is made at the front-end processing for feature vector selection by applying the silence region calibration algorithm for start and end point detection. The classifier had also been modified significantly by incorporating Viterbi search with Genetic Algorithm (GA) to obtain high accuracy in recognition result and for lexical unit classification. The results were evaluated by following National Institute of Standards and Technology (NIST) benchmarking. Based on the test, it shows that the recognition accuracy has been improved by 30% to 40% using Genetic Algorithm technique compared with conventional technique. A new corpus had been built with verification and justification from the medical expert in this study. In conclusion, computerized method for early screening can ease human effort in tackling speech disorders and the proposed Genetic Algorithm technique has been proven to improve the recognition performance in terms of search and classification task
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