24,548 research outputs found
Prosodic Event Recognition using Convolutional Neural Networks with Context Information
This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surrounding context. We show that adding position features indicating
the current word benefits the CNN. In addition, this paper discusses the
generalization from a speaker-dependent modelling approach to a
speaker-independent setup. The proposed method is simple and efficient and
yields strong results not only in speaker-dependent but also
speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur
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
Speech vocoding for laboratory phonology
Using phonological speech vocoding, we propose a platform for exploring
relations between phonology and speech processing, and in broader terms, for
exploring relations between the abstract and physical structures of a speech
signal. Our goal is to make a step towards bridging phonology and speech
processing and to contribute to the program of Laboratory Phonology. We show
three application examples for laboratory phonology: compositional phonological
speech modelling, a comparison of phonological systems and an experimental
phonological parametric text-to-speech (TTS) system. The featural
representations of the following three phonological systems are considered in
this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English
(SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded
speech, we conclude that the latter achieves slightly better results than the
former. However, GP - the most compact phonological speech representation -
performs comparably to the systems with a higher number of phonological
features. The parametric TTS based on phonological speech representation, and
trained from an unlabelled audiobook in an unsupervised manner, achieves
intelligibility of 85% of the state-of-the-art parametric speech synthesis. We
envision that the presented approach paves the way for researchers in both
fields to form meaningful hypotheses that are explicitly testable using the
concepts developed and exemplified in this paper. On the one hand, laboratory
phonologists might test the applied concepts of their theoretical models, and
on the other hand, the speech processing community may utilize the concepts
developed for the theoretical phonological models for improvements of the
current state-of-the-art applications
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Variable Word Rate N-grams
The rate of occurrence of words is not uniform but varies from document to
document. Despite this observation, parameters for conventional n-gram language
models are usually derived using the assumption of a constant word rate. In
this paper we investigate the use of variable word rate assumption, modelled by
a Poisson distribution or a continuous mixture of Poissons. We present an
approach to estimating the relative frequencies of words or n-grams taking
prior information of their occurrences into account. Discounting and smoothing
schemes are also considered. Using the Broadcast News task, the approach
demonstrates a reduction of perplexity up to 10%.Comment: 4 pages, 4 figures, ICASSP-200
Robustness issues in a data-driven spoken language understanding system
Robustness is a key requirement in spoken language understanding (SLU) systems. Human speech is often ungrammatical and ill-formed, and there will frequently be a mismatch between training and test data. This paper discusses robustness and adaptation issues in a statistically-based SLU system which is entirely data-driven. To test robustness, the system has been tested on data from the Air Travel Information Service (ATIS) domain which has been artificially corrupted with varying levels of additive noise. Although the speech recognition performance degraded steadily, the system did not fail catastrophically. Indeed, the rate at which the end-to-end performance of the complete system degraded was significantly slower than that of the actual recognition component. In a second set of experiments, the ability to rapidly adapt the core understanding component of the system to a different application within the same broad domain has been tested. Using only a small amount of training data, experiments have shown that a semantic parser based on the Hidden Vector State (HVS) model originally trained on the ATIS corpus can be straightforwardly adapted to the somewhat different DARPA Communicator task using standard adaptation algorithms. The paper concludes by suggesting that the results presented provide initial support to the claim that an SLU system which is statistically-based and trained entirely from data is intrinsically robust and can be readily adapted to new applications
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