2,610 research outputs found
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.Comment: NIPS 201
A High Quality Text-To-Speech System Composed of Multiple Neural Networks
While neural networks have been employed to handle several different
text-to-speech tasks, ours is the first system to use neural networks
throughout, for both linguistic and acoustic processing. We divide the
text-to-speech task into three subtasks, a linguistic module mapping from text
to a linguistic representation, an acoustic module mapping from the linguistic
representation to speech, and a video module mapping from the linguistic
representation to animated images. The linguistic module employs a
letter-to-sound neural network and a postlexical neural network. The acoustic
module employs a duration neural network and a phonetic neural network. The
visual neural network is employed in parallel to the acoustic module to drive a
talking head. The use of neural networks that can be retrained on the
characteristics of different voices and languages affords our system a degree
of adaptability and naturalness heretofore unavailable.Comment: Source link (9812006.tar.gz) contains: 1 PostScript file (4 pages)
and 3 WAV audio files. If your system does not support Windows WAV files, try
a tool like "sox" to translate the audio into a format of your choic
Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible
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