11,903 research outputs found
Representation Analysis Methods to Model Context for Speech Technology
Speech technology has developed to levels equivalent with human parity through the use of deep neural networks. However, it is unclear how the learned dependencies within these networks can be attributed to metrics such as recognition performance. This research focuses on strategies to interpret and exploit these learned context dependencies to improve speech recognition models. Context dependency analysis had not yet been explored for speech recognition networks.
In order to highlight and observe dependent representations within speech recognition models, a novel analysis framework is proposed. This analysis framework uses statistical correlation indexes to compute the coefficiency between neural representations. By comparing the coefficiency of neural representations between models using different approaches, it is possible to observe specific context dependencies within network layers. By providing insights on context dependencies it is then possible to adapt modelling approaches to become more computationally efficient and improve recognition performance. Here the performance of End-to-End speech recognition models are analysed, providing insights on the acoustic and language modelling context dependencies. The modelling approach for a speaker recognition task is adapted to exploit acoustic context dependencies and reach comparable performance with the state-of-the-art methods, reaching 2.89% equal error rate using the Voxceleb1 training and test sets with 50% of the parameters. Furthermore, empirical analysis of the
role of acoustic context for speech emotion recognition modelling revealed that emotion cues are presented as a distributed event. These analyses and results for speech recognition applications aim to provide objective direction for future development of automatic speech recognition systems
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Multilingual Speech Recognition With A Single End-To-End Model
Training a conventional automatic speech recognition (ASR) system to support
multiple languages is challenging because the sub-word unit, lexicon and word
inventories are typically language specific. In contrast, sequence-to-sequence
models are well suited for multilingual ASR because they encapsulate an
acoustic, pronunciation and language model jointly in a single network. In this
work we present a single sequence-to-sequence ASR model trained on 9 different
Indian languages, which have very little overlap in their scripts.
Specifically, we take a union of language-specific grapheme sets and train a
grapheme-based sequence-to-sequence model jointly on data from all languages.
We find that this model, which is not explicitly given any information about
language identity, improves recognition performance by 21% relative compared to
analogous sequence-to-sequence models trained on each language individually. By
modifying the model to accept a language identifier as an additional input
feature, we further improve performance by an additional 7% relative and
eliminate confusion between different languages.Comment: Accepted in ICASSP 201
- …